Virtual Water Consumption and Its Relationship to Future Food Security in Singapore

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VIRTUAL WATER CONSUMPTION AND ITS RELATIONSHIP TO FUTURE FOOD SECURITY IN SINGAPORE

LIM ZHONG YI

DEPARTMENT OF CIVIL & ENVIRONMENTAL ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE

2014/2015 VIRTUAL WATER CONSUMPTION AND ITS RELATIONSHIP TO FUTURE FOOD SECURITY IN SINGAPORE

LIM ZHONG YI

A THESIS SUBMITTED

FOR THE DEGREE OF BACHELOR OF ENGINEERING

DEPARTMENT OF CIVIL & ENVIRONMENTAL ENGINEERING

NATIONAL UNIVERSITY OF SINGAPORE ACKNOWLEDGEMENTS

First, I would like to express my sincere gratitude to my supervisor Assistant Professor

Yeh Jen-Feng Pat for his continual encouragement, guidance and support. He never fails to steer me in the right direction when I get misplaced in a constellation of concepts and ideas. I am deeply grateful for the opportunity to learn from him.

I would also like to thank Associate Professor Vladan Babovic for his invaluable suggestions and comments during the presentation. His immense experience greatly guided the formulation of this dissertation proposal.

This thesis would not be possible without the guidance and experience of all the professors in NUS Civil & Environmental Engineering Department whom I have encountered in one way or another. I would like to acknowledge Dr. Naota Hanasaki from National Institute for Environmental Studies, Japan and Ms Fritzi Anne Girado

Gironella of NUS for their assistance and support. I would also like to thank my parents and two elder sisters. They were always supporting me and encouraging me with their best wishes.

Last but not least, I am thankful for having recognized my interest in this topic and I am grateful for the opportunity to nurture my passion in NUS. The formulation of this proposal is not the result of my work alone but also the collective effort of everyone who have assisted me in one way or another.

TABLE OF CONTENTS

SUMMARY ...... v

NOMENCLATURE ...... vii

LIST OF FIGURES ...... viii

LIST OF TABLES ...... ix

CHAPTER 1 – INTRODUCTION ...... 1

1.1 Background ...... 1

1.2 Food trade & consumption in Singapore ...... 2

1.3 Objectives of study ...... 3

1.4 Chapter synopsis ...... 4

CHAPER 2 – LITERATURE REVIEW ...... 5

2.1 The concept of virtual water ...... 5

2.2 Food security ...... 8

2.3 Water and sustainability ...... 9

CHAPTER 3 – METHODOLOGY ...... 10

3.1 Scope of the water footprint study ...... 10

3.2 Agricultural products selected in the study ...... 11

3.3 Calculating virtual water trade flows ...... 11

3.4 Falkenmark Water Stress Index ...... 11

3.5 Water Scarcity Index ...... 12

3.6 Water Poverty Index ...... 13

3.7 Data sources ...... 15

3.7.1 Population data ...... 15

3.7.2 Farming systems ...... 15

3.7.3 Trade data for primary crops and livestock ...... 15

3.7.4 Virtual water content of agricultural products ...... 16

3.7.5 Physical, socio-economic and environmental data ...... 16

CHAPTER 4 – SINGAPORE’S FOOD CONSUMPTION & SUPPLY...... 17

4.1 Historical food consumption patterns ...... 17

4.2 Food import quantity per year ...... 18

4.3 Food import quantity per product ...... 18

4.4 Food import quantity per country ...... 20

CHAPTER 5 – VIRTUAL WATER CONTENT OF CROPS & LIVESTOCK ...... 22

5.1 Virtual water content of agricultural products ...... 22

5.2 Virtual water content of live animals by farming systems ...... 23

CHAPTER 6 – SINGAPORE’S VIRTUAL WATER IMPORT FLOWS ...... 26

6.1 Comparison of results with earlier study ...... 26

6.2 Virtual water import flows per year ...... 26

6.3 Virtual water import flows per product ...... 27

6.4 Virtual water import flows per country ...... 28

CHAPTER 7 – AGRICULTURE AND WATER RESOURCES ...... 31

7.1 Global distribution of freshwater resources ...... 31

7.2 Falkenmark water stress index ...... 32

7.3 Virtual water flows related to trade in agricultural products ...... 33 iii

7.4 Water poverty index ...... 35

CHAPER 8 – DISCUSSION ...... 38

8.1 Study implications ...... 38

8.2 Limitations of the study ...... 39

CHAPTER 9 – RECOMMENDATIONS AND CONCLUSIONS ...... 41

9.1 Recommendations ...... 41

9.2 Conclusions ...... 43

REFERENCES ...... 45

APPENDICES ...... 48

Appendix A – Crops and livestock products included in this study ...... 48

Appendix B – Singapore annual population and growth rate ...... 50

Appendix C – Quantity of main food items traded in Singapore ...... 52

Appendix D – Per capita consumption of main food items in Singapore ...... 54

Appendix E – Annual food import quantity ...... 55

Appendix F – Virtual water content estimates of agricultural products ...... 60

Appendix G – Annual gross virtual water import flows ...... 66

Appendix H – Falkenmark Water Stress Index ...... 71

Appendix I – Water Scarcity Index ...... 72

Appendix J – Water Poverty Index ...... 73

Appendix K – Virtual water flows related to trade in agricultural products ...... 78

SUMMARY

Singapore’s population increased from about 1.65 million in the 1960s to 5.49 million currently. The number is expected to reach 7 million by 2050 (FAO, 2015). As the country with high per capita income of US$55,183 (World Bank, 2013 data),

Singapore is considered to be relatively food secured. However, the country is still vulnerable to food unavailability due to heavily reliance on imported food.

Over 60 % of the world’s freshwater supply is found in just 9 countries and agriculture accounted for 70% of global freshwater withdrawals. Increasing water scarcity through excessive use of freshwater and mismanagement of the available water resources are major concerns for agricultural sustainability, especially in arid and semi – arid region.

Virtual water trade in the form of goods can be used a strategic instrument to achieve water security and improve the imbalance distribution of global freshwater supply in these countries. The study seeks to quantify virtual water import flows in Singapore using the virtual water method and evaluate the degree of water resources vulnerability of various food-producing countries.

In the period between 2002 and 2011, the gross total virtual water consumption in

Singapore through agricultural products was 3,473 million m3 per year. The estimates in this study cover virtual water consumption as a result of trade in 49 primary crops and 7 livestock products commonly consumed in Singapore. The estimates do not include virtual water import flows as a result of trade in processed (crops & livestock) and industrial products, which would contribute to even greater virtual water import volumes. Agricultural products that contributed largely to the gross total virtual water consumption here are rice (26%), chicken (14%), swine (12%), fruit (11%), wheat

(10%), beef cattle (9%), hen eggs (5%) & vegetables (4%). Six countries accounting for 80% of gross total virtual water consumption are Thailand (778 Mm3/yr), Malaysia

(691 Mm3/yr), Brazil (586 Mm3/yr), Australia (412 Mm3/yr), USA (217 Mm3/yr) and

India (168 Mm3/yr) in the period.

The Water Poverty Index (WPI) is a holistic approach used to measure the relative position of a country in providing water. It takes into consideration the water use by industry and agriculture to incorporate the importance of water need for food and production. 20 major food producing countries by gross total virtual water consumption in Singapore were calculated. The five ‘most water sustainable’ countries by standing are Netherlands (70.06), Canada (67.60), New Zealand (64.66), Australia

(63.89) and France (60.83).

NOMENCLATURE

ASEAN Association of Southeast Asian Nations AVA Agri-Food & Veterinary Authority of Singapore CT Commodity Trade FAO Food and Agriculture Organization GDP Gross Domestic Product Gm3 Giga cubic metres GVWI Gross Virtual Water Import GVWE Gross Virtual Water Export ha hectare IRWR Internal Renewable Water Resources kg kilogram l/p/d Litres per person per day Mm3 Mega cubic metres NVWI Net Virtual Water Import PPP Purchasing Power Parity PUB Public Utilities Board, Singapore ton tonne UNDP United Nations Development Programme VWC Virtual Water Content VWT Virtual Water Trade WA Water Availability WF Water Footprint WPI Water Poverty Index WS Water Scarcity WU Water Use yr Year

vii

LIST OF FIGURES

Descriptions Page Figure 1.1 Annual population comprises Singapore residents and non- 1 residents. Figure 2.1 Steps to calculate global virtual water trade related to crops 6 Figure 2.2 Steps to calculate global virtual water trade related live animals 7 and livestock products Figure 2.3 Basic components of food security 8 Figure 3.1 Workflow of study 10 Figure 4.1 Composition of food commodities traded in two periods 17 Figure 4.2 Contribution of various food commodities to the total sum of 19 Singapore’s annual food import quantity Figure 4.3 Food composition of annual food import quantity per food 21 producing country Figure 5.1 Global average virtual water footprint of agricultural products 22 Figure 5.2 Crop yield of rice in various countries 23 Figure 5.3 Average virtual water footprint of paddy rice 23 Figure 5.4 Global average virtual water content of live animals per 24 farming system Figure 6.1 Virtual water import flows by countries (time – series) 26 Figure 6.2 Contribution of various food commodities to the gross total 27 virtual water consumption in Singapore Figure 6.3 Food composition of virtual water import flows per country 30 Figure 7.1 Breakdown of global freshwater withdrawals and global 31 freshwater resources distribution. Figure 7.2 Per capita freshwater availability of various food producing 32 countries Figure 7.3 Logarithmic diagram showing water demand and water 33 availability of various food-producing countries. Figure 7.4 Virtual – water flows related to trade in agricultural products 33 per capita Figure 7.5 Water Poverty Index of various selected countries 35 Figure 7.6 Composition of virtual water import flows for the ‘most and 37 least water sustainable’ countries. Figure 8.1 Composition of virtual water import flows under different 39 water conditions

viii

LIST OF TABLES

Descriptions Page Table 1.1 Distribution of annual food imports & per capita consumption in 2 Singapore Table 3.1 Thresholds of Falkenmark Index 12 Table 3.2 Characterization of water stress (Criticality Ratio) 12 Table 3.3 Components and sub-indices of Water Poverty Index 14 Table 3.4 Comparison of a few data from AVA and FAO databases 16 Table 4.1 Composition of food commodities traded in two periods 18 Table 4.2 Annual food imports quantity 18 Table 4.3 Contribution of various food commodities to the total sum of 19 Singapore’s annual food import quantity Table 4.4 Annual food import quantity from major food producing 20 countries Table 5.1 Global average virtual water content of live animals per farming 24 system Table 5.2 Average annual water footprint of one animal per animal 25 category Table 6.1 Comparison of results with earlier study (D. Vanham, 2011) 26 Table 6.2 Contribution of various food commodities to the gross total 28 virtual water consumption in Singapore Table 6.3 Annual virtual water import flows related to agricultural products 28 from major food producing countries Table 7.1 Top 5 gross virtual water importers and exporters related to food 34 and agricultural products Table 7.2 The Water Poverty Index and sub-components 36

CHAPTER 1 – INTRODUCTION

1.1 Background

Singapore’s population increased from about 1.65 million in the 1960s to 5.49 million currently. The number is expected to reach 7 million by 2050 [Fig. 1.1]. Due to physical constraint, Singapore imports 90% of her food requirement from more than

15 countries around the world, with an increasing percentage sources from ASEAN member states (Appendix E.3).

8,000

7,000

6,000

5,000

4,000

3,000

Total populationTotal('000) 2,000

1,000

1974 1960 1962 1964 1966 1968 1970 1972 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2030 2050 Year

Figure 1.1: Annual population comprises Singapore residents and non-residents. Resident population comprises Singapore citizens and permanent residents. Data source: Singapore Department of Statistics; FAO (2015).

As the country with high per capita income of US$55,183 (World Bank, 2013 data),

Singapore is considered to be relatively food secured. However, the country is still vulnerable to food unavailability due to heavily reliance on imports for food. Any disruptions of food supply from her major food importing partners as a result of extreme events such as drought or flooding will reduce total food supply. Food – producing countries will need to feed their local demands first before exporting any surplus to other countries.

As part of the effort to enhance food security, Singapore government support local production of key food items such as leafy vegetable, eggs and seafood to increase capacity, consumption and hence, food self-sufficiency. AVA targets to increase self- production by 30% of eggs, 15% of fish and 10% of leafy vegetables consumed in

Singapore. At present, local farming account for 23% of eggs, 4% of fish and 7% of leafy vegetables consumed (AVA, 2014).

Despite government effort to optimise local food productivity, local production is not sufficient if food supply is disrupted for prolonged period. Singapore is still vulnerable to the volatility in global food price and supply. As such, Singapore government actively seeks to diversify sources of food supply instead of relying on a particular source.

1.2 Food trade & consumption in Singapore

Table 1.1: Distribution of annual food imports & per capita consumption in Singapore

Average food import quantity Per capita consumption

Food items ton/yr Share (%) kg/cap/yr Vegetables 426,645 26.15 91 Fruits 384,596 23.57 77 Rice 303,314 18.59 68 Wheat 166,104 10.18 37 Chicken 154,320 9.46 32 Pork 91,361 5.60 20 Hen eggs 58,713 3.60 13 Beef 22,416 1.37 4 Duck 14,239 0.87 3 Mutton 10,077 0.62 2 Total 1,631,785 100% 347 Data source: Wheat, rice & hen eggs FAO (2015), rest of food items from AVA, Singapore (2015). Population average for period 2002 – 2011 use 4.58 million from Singapore Department of Statistics.

Table 1.1 shows the distribution of annual food imports and per capita consumption of major food commodities in Singapore during the period 2002 – 2011. It is apparent that vegetable, fruits, rice and wheat accounted for majority of food quantity imported.

[Appendix C].

Among the livestock products, Singaporeans generally preferred white meat (chicken

& pork) over red meat (beef & mutton). From the statistics, average annual per capita consumption of chicken & pork was 32 kg & 20 kg respectively. In contrast, average annual per capita consumption of beef & mutton was 4 kg & 2 kg respectively.

Vegetables & fruits accounted for large proportion of Singaporean diet. The average annual per capita consumption of vegetables & fruits was recorded at 91 kg & 77 kg respectively, showing a strong demand for healthy food items [Appendix D].

1.3 Objectives of study

The study focused on quantifying virtual water import related to the trade in agricultural (crop and livestock) products in Singapore. The study looked at how dietary change over the last five decades influenced virtual water import flows. The estimates in this study covered virtual water import flows as a result of trade in major agricultural commodities during the period 2002 – 2011. The following objectives were proposed in the study:

1. To estimate the annual virtual water import volumes in Singapore during the

period;

2. To identify the food producing countries & agricultural commodities

responsible for the largest annual virtual water volumes imported;

3. To evaluate the degree of water resources vulnerability (water stress and

scarcity) of selected food – producing countries using various indices and

identify the risks of future food security in Singapore.

1.4 Chapter synopsis

Chapter 2 consists of literature reviews related to the topic – the concept of virtual water, food security and water sustainability. A description of research methodologies and data used in this study are outlined in Chapter 3. Chapter 4 presents the composition of Singapore’s food supply; chapter 5 studies the virtual water content estimates of agricultural commodities; chapter 6 examines the gross total virtual water consumption in Singapore for the period analyzed; chapter 7 evaluates the vulnerability of water resources in selected food – producing countries using various indices; chapter 8 discusses the implications of water resources vulnerability to

Singapore future’s food security and limitations of this study. The paper will conclude with recommendations for future research.

CHAPER 2 – LITERATURE REVIEW

This chapter contains the concept of virtual water, food security and water resources sustainability. The section also review the methodology of previous studies, in particular Mekonnen and Hoekstra (2010) and draw on recent studies made by the other authors and list some of the essential points that need to be considered when assessing virtual water.

2.1 The concept of virtual water

Virtual water refers to the volume of water required to produce a unit mass of the product (Allan, 1998). For example, when a country imports one ton of maize, it is importing virtual water. Virtual water trade is suggested as a way to alleviate the uneven global distribution of freshwater resources by allowing water resources to transfer from water-rich to water-scarce countries. This can also help to achieve greater water use efficiency as virtual water flows from relatively more efficient countries to less-efficient ones.

The virtual water content of a crop can be calculated by dividing the total water required for evapotranspiration by the crop yield. The value is affect by the climatic parameters of each food producing country. The schematic diagram of the steps to calculate specific water demand and hence, the virtual water trade flows between countries is shown below [Fig. 2.1].

Figure 2.1: Steps to calculate global virtual water trade related to crops

Virtual water content of animals is the volume of water required throughout the lifetime for feed production, drinking and cleaning before they are slaughtered for consumption. To illustrate this concept, it takes about 15,712 m3 of water to produce one ton of beef. This value varies depending on the type of production systems which the beef is derived from (grazing, mixed or industrial) as well as the composition and origin of feed. Industrial production system is generally more water efficient due to large feed conversion efficiency. The system produce higher yield compared to grazing or mixed and is the dominant system worldwide (Gerbens-Leenes et al., 2011). The schematic diagram of the steps to calculate virtual water content related to live animals and livestock products is shown below [Fig. 2.2].

Figure 2.2: Steps to calculate global virtual water trade related live animals and livestock products

Water footprint has a broader scope and refers not only to the volume of water, but also to the type of water (green, blue or grey) used, when and where the water was used. The green water is the rain fed component; blue water is the freshwater component and grey water assimilate the load pollutant. Industrial farming generally consists of a large part of blue and grey water compared to grazing or mixed system.

However, blue water scarcity is often associated with freshwater consumption and pollution. Thus, industrial farming may not be favourable from a water resource point of view.

2.2 Food security

FAO defined food security as a condition when “all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life” (FAO, 1996). The concept of food security depends on three facets of food system: availability of food, access to food and utilisation of food (FAO, 2000).

Food Availability Food Access Food Utilization

Production Affordability Nutrition

Distribution Allocation Safety

Exchange Preference Social Value

Figure 2.3: Basic components of food security

The availability of food addresses the ‘supply side’ of food security and is dependent on the level of food production, stock levels and net trade. Food availability is also a component whereby food security is directly affected by climate variability

(Thompson et al., 2010).

Access to food is determined by physical and financial resources as well as social and political factors of a country (Ericksen et al., 2011). It is affected by the affordability of food, allocation of food and preferences for food.

Utilization of food is reflected in the nutritional status of an individual and food safety.

It requires the population to have sufficient nutrients and balanced diet. The call for stability in the availability, access, and utilization of food imply that people need to

8 feel secure about their future food supply in order to achieve food security in their country.

2.3 Water and sustainability

Water resources are depleting at a distressing rate around the world. Globally, agricultural production accounted for 70% of water use (World Bank, 2013 data).

Increasing water scarcity through excessive use of water and mismanagement of the available water resources are major concerns for agricultural sustainability, especially in arid and semi-arid regions. In addition, growing competition between different sectors increase pressure on water use, limit food production and threaten food security worldwide.

A relation between freshwater resources availability and the ability to produce food exists. In water – scarce region, countries depend on virtual water imports to relieve pressure on their water resources. Cereal grains contributed to the largest share of food imported to most water – scarce countries (Yang and Zehnder 2002). Yang et al. (2003) suggested that with the strong relationship between the volume of available freshwater resources and the quantity of food imported, a model can be proposed to provide a distinction between water – scarce and water-abundant nations. Virtual water trade in the form of agricultural and industrial products can be used as a strategic instrument to achieve water security in water scarce countries, alleviate the imbalance distribution of global freshwater supply and achieve global water use efficiency.

CHAPTER 3 – METHODOLOGY

In this section, the methodological steps to estimate the virtual water import flows are presented and discussed. The virtual water consumption related to agricultural products is calculated using statistical data from online databases (trade data) and literature review (virtual water content estimates). In this way, the virtual water import flows in Singapore can be quantified and discussed.

3.1 Scope of the water footprint study

The study serves as a preliminary assessment to evaluate water resources and food production, in particular, the impacts on Singapore’s future food security. The estimates do not include processed (crops and livestock) and industrial products which would result in higher virtual water import volumes. It should be noted that water productivity is variable in space and time. In this study, a set of constant virtual water values were drawn from the work of Mekonnen and Hoekstra (2010). The ultimate goal here is to present a comprehensive study on virtual water consumption pattern in

Singapore which can be used for future research.

Degree of Virtual water Annual water content Gross annual Implications quantity of resources estimates of virtual water on future food vulnerability food items import flows food security imported in in food- by country- in Singapore in Singapore Singapore producing of-origin countries

Figure 3.1: Workflow of study

3.2 Agricultural products selected in the study

49 crop commodities and 7 livestock commodities commonly consumed in Singapore were selected for the period between 2002 and 2011. The selected food commodities are classified into 5 broad categories – cereals, vegetables, fruits, roots and livestock

[Appendix A].

3.3 Calculating virtual water trade flows

Virtual water trade of agricultural products

The Virtual Water Trade [VWT] of a commodity [c] is given by the multiplication of the quantity of Commodity Traded [CT] and Virtual Water Content [VWC] of the commodity from country origin.

(3.1)

Gross virtual water import

The Gross Virtual Water Import [GVWI] is the sum of all virtual water of commodity

[c] imported in year [t].

(3.2)

3.4 Falkenmark Water Stress Index

Falkenmark et al. (1989) describe water stress as annual water availability per person.

The stress implication of a country can be categorized as: no stress, stress, scarcity, and absolute scarcity [Table 3.1]. Index thresholds of 1,700m3 and 1000m3 per capita per year are used to differentiate between water stressed and water scarce countries respectively. Below 500m³ per capita per year water availability is a major constraint to life (Falkenmark, 1989).

Table 3.1: Thresholds of Falkenmark Index

Original Falkenmark Adapted ‘ water scarcity’ Water stress implication indicator for water index or ‘Falkenmark crowding indicator’ Persons per flow unita/yr m3/capita/yr < 600 > 1700 No Stress 600 - 1000 1000 - 1700 Stress 1000 - 2000 500 - 1000 Scarcity > 2000 < 500 Absolute Scarcity a One flow unit = one million cubic metre

The idea behind this index is to understand how much water is required to meet human demands so that the water availability to each person can be used to serve as a measure of scarcity on a national scale (Rijsberman, 2006).

3.5 Water Scarcity Index

The index of national Water Scarcity [WS] is the Water Use [WU] as percentage of the total Water Available [WA] in a country. As a measure of the water resources availability, WA is taken as the total renewable water resources and for practical reason, only blue water withdrawal is accounted for (Hoekstra and Hung, 2002).

(3.3)

The severity of water stress can be characterised as follow:

Table 3.2: Characterization of water stress (Criticality Ratio)

Percent withdrawal Technical water stress <10 Low water stress 10 – 20 Medium low water stress 20 – 40 Medium high water stress >40 High water stress Source: UN/WMO/SEI 1997

The relationship between total water resources available and water withdrawals indicates the degree of mobilization when comparing with the total water resources

12 available (Falkenmark et al, 2004). The “Criticality Ratio” (CR) expresses the technical and economic difficulties and indicates that when the percent withdrawal exceeds a threshold of 40% a condition of severe water stress occurs (Alcamo et al.,

2000).

3.6 Water Poverty Index

The Water Poverty Index (WPI) is a comprehensive method used to measure the relative position of a country in providing water (Lawrence et al. 2002). WPI consists of 5 indices (resources, access, capacity, use and environment). Each indices consists of a few sub-indices which are normalised to a range of scales between 0 and 1 using the formula below unless otherwise mentioned. The sub-indices within each index are averaged and multiplied by 20. The final index score for WPI is the sum of all five indices in the range of 0 to 100.

(3.4)

xi denotes the original value of country i, xmax is the country with the highest value and xmin is the country with lowest value. The structure of index and their sub-indices are shown below [Table 3.3].

Table 3.3: Components and sub-indices of Water Poverty Index

Components Sub-indices

1. Resources  Sum of internal and external renewable freshwater resources per capita (m3/cap/yr) expressed on log scale.  An arbitrary factor of 0.5 is applied on external renewable freshwater resources to reduce weight as these resources are generally less secured compared to internal water resources.

2. Access  Access to improved water sources (%)  Access to improved sanitation facilities (%)  Percentage of arable land equipped for irrigation (%)

3. Capacity  GDP per capita, PPP (US$)  Mortality rate, under – 5 (per 1,000 live births)  UNDP Education index  Gini index

4. Use  Domestic freshwater withdrawals per capita (litres daily per capita consumption)

< 50 litres daily per capita consumption : Index = xi/50

Between 50 – 150 litres daily per capita consumption: Index = 1 – [( xmin – 50)/(xmax – 50)]

> 150 litres daily per capita consumption: Index = 1 – [( xmin – 50)/(xmax – 150)]

 Industrial water use efficiency is calculated as:

 Agricultural water use efficiency is calculated as:

The higher the ratio, the higher the water use efficiency by the sectors.

5. Environment  Fertilizer consumption (kg/ha of arable land)  Agricultural methane emissions (% of total)

3.7 Data sources

3.7.1 Population data

Annual population data for the period 1960 – 2011 were collected from the Singapore

Department of Statistics [Appendix B]. Annual population comprises Singapore residents & non-residents. Resident population comprises Singapore citizens and permanent residents.

3.7.2 Farming systems

FAO categorises the livestock production systems into three major types: grazing, mixed and industrial systems. In general, grazing system has the lowest stock yield and supply only 9% of global livestock production. Mixed production system which combined livestock farming and crop farming accounted for 54% of total meat produced in the world. Mixed cattle system is the dominant system for example in

Brazil, China, Ethiopia, India, New Zealand and the US. Industrial system supply more than half of global pork and poultry meats and 10% for beef and mutton (FAO, 1996).

3.7.3 Trade data for primary crops and livestock

FAO database is a major source of data used in this study. The database contains data related to food production, trade and prices of major agricultural commodities from most countries in the world between years 1960 – 2011. Table 3.4 shows a comparison of data from AVA and FAO database. The results show slight discrepancy in the values reported due to estimations and assumptions made for missing data in the FAO database. Overall, FAO data compared very well with AVA statistics and FAO data will be adopted throughout the study [Appendix E].

Table 3.4: Comparison of a few data from AVA and FAO databases

Average food import quantity Per capita consumption Food (ton/yr) (kg cap-1yr-1) items AVA Share FAO Share AVA FAO (%) (%) Vegetables 426,645 26.15 401,712 24.05 91 88 Fruits 384,596 23.57 309,541 18.53 77 68 Rice 303,314 18.59 303,314 18.16 68 66 Wheat 166,104 10.18 166,104 9.95 37 36 Chicken 154,320 9.46 172,450 10.33 32 38 Pork 91,361 5.60 59,952 3.59 20 13 Hen eggs 58,713 3.60 58,713 3.52 13 13 Beef 22,416 1.37 19,583 1.17 4 4 Duck 14,239 0.87 12,455 0.75 3 3 Mutton 10,077 0.62 9,299 0.56 2 2 Others - - 156,951 9.40 - - Total 1,631,786 100.00 1,670,074 100.00 Data source: AVA, Singapore and FAO (2015)

3.7.4 Virtual water content of agricultural products

Data of virtual water estimates related to agricultural products selected by country of origin were adapted from the work of Mekonnen and Hoekstra (2010). The set of data, by far, contains the most comprehensive virtual water values of various food commodities from most countries in the world. It should be noted that these values vary according to country origin and productivity. Generally, the virtual water content in livestock products is higher than crop products [Appendix F].

3.7.5 Physical, socio-economic and environmental data

The physical, socio-economic and environmental data used to calculate the various water resource indices are obtained from various sources such as World Bank and

FAO databases [Appendix J].

CHAPTER 4 – SINGAPORE’S FOOD CONSUMPTION & SUPPLY

4.1 Historical food consumption patterns

1965 - 1975 2002 - 2011 80 70 70 60 50 40 30

Share (%) Share 30 24 19 21 20 15 6 9 10 4 3 0 Cereals Roots Vegetables Fruits Livestock

Figure 4.1: Composition of food commodities traded in two periods

Figure 4.1 shows the composition of food traded between the 2 periods set. The 1st period used is 1965 – 1975 where most countries seek to achieve food self sufficiency and the 2nd period 2002 – 2011 is the study period. The average values in each period are calculated to account for any seasonal variation.

Singapore food consumption patterns shifted from cereal crops to livestock products.

Demand for meat products has increased over the last few decades to 21% of food imported compared to 3% forty years ago [Table 4.1]. The significant change in share of meat is largely due to higher income and the ethnic diversity of the population.

Although cereal consumption reduced from 70 % to 30%, rice remains as the main staple food in Singaporean diets (18% of total food imported).

Table 4.1: Composition of food commodities traded in two periods

Period 1965 – 1975 2002 – 2011 Food items ton/yr Share (%) ton/yr Share (%) Cereals 708,016 69.52 499,087 29.88 Roots 40,404 3.97 107,302 6.42 Vegetables 90,793 8.92 401,712 24.05 Fruits 149,016 14.63 309,541 18.53 Livestock 30,164 2.96 352,432 21.10 Total 1,018,392 100.00 1,670,074 100.00 Data source: FAO (2015)

4.2 Food import quantity per year

Singapore food import quantity during the period 2002 – 2011 related to trade in crop and livestock products was 1,670,074 tonnes per year. Table 4.2 shows the annual quantity of food imported each year [Appendix E.1].

Table 4.2: Annual food imports quantity

Crop products Livestock products Total Year (ton/yr) (ton/yr) (ton/yr) 2002 1,407,925 251,476 1,661,403 2003 1,301,413 284,944 1,588,360 2004 1,255,483 285,609 1,543,096 2005 1,255,735 343,549 1,601,289 2006 1,254,021 344,303 1,600,330 2007 1,297,604 390,060 1,689,671 2008 1,289,210 410,614 1,701,832 2009 1,323,948 404,414 1,730,371 2010 1,367,209 419,546 1,788,765 2011 1,423,871 389,811 1,815,693 Average 1,317,642 352,432 1,670,074

4.3 Food import quantity per product

Vegetables form the largest share of food items imported by quantity (24%), followed by fruits (19%), rice (18%), chicken & wheat (10 % each). The import quantity by food commodities is shown in Figure 4.2 [Appendix E.2].

Figure 4.2: Contribution of various food commodities to the total sum of Singapore’s annual food import quantity

Table 4.3: Contribution of various food commodities to the total sum of Singapore’s annual food import quantity

Food Items ton/yr Share (%) Cereals 499,087 29.88 Wheat 166,104 9.95 Rice 303,314 18.16 Cereal unmilled 7,292 0.44 Barley 396 0.02 Maize 19,914 1.19 Oats 222 0.01 Oats rolled 1,845 0.11

Vegetables 401,712 24.05

Livestock 352,432 21.10 Beef Cattle 19,583 1.17 Goat 19,979 1.20 Sheep 9,299 0.56 Chicken 172,450 10.33 Duck 12,455 0.75 Pork 59,953 3.59 Hen eggs 58,713 3.52

Fruits 309,541 18.53 Roots and tubers 107,302 6.42 Total 1,670,074 100.00

4.4 Food import quantity per country

The annual food import quantity for the period 2002 – 2011 has been calculated for each food – producing countries. The list of countries which contributed to 80% of

Singapore annual food import quantity is listed in Table 4.4.

Table 4.4: Annual food import quantity from major food producing countries

No. Countries ton/yr Share (%) Cum. % 1. Malaysia 484,397 29.00 29.00 2. Thailand 266,482 15.96 44.96 3. China 196,686 11.78 56.74 4. Australia 164,135 9.83 66.57 5. USA 136,291 8.16 74.73 6. Brazil 98,022 5.87 80.60 Others 324,060 19.40 100.00 Total 1,670,074 100.00

Table 4.4 shows the ranking of annual food import quantity from the respective food producing countries. The top six countries in term of food quantity in tonnes per year imported are: Malaysia (484,397), Thailand (266,482), China (196,686), Australia

(164,135), USA (136,291) and Brazil (98,022). Malaysia contributed to the largest quantity of total food items imported (29%) during the period. The food composition imported from Malaysia is as followed: Vegetable (45.8%), livestock (30.6%), fruits

(21.6%), cereals (1.1%), roots and tubers (0.9%). The food composition of top ten countries in relation to the annual food quantity imported for the period is shown in

Figure 4.3 [Appendix E.3].

600,000

500,000

400,000

300,000 ton/yr

200,000

100,000

0 Malaysia Thailand China Australia USA Brazil Viet Nam India Indonesia Canada Cereals Roots Fruits Livestock Vegetables

Figure 4.3: Food composition of annual food import quantity per food producing country

CHAPTER 5 – VIRTUAL WATER CONTENT OF CROPS & LIVESTOCK

5.1 Virtual water content of agricultural products

The amount of embedded water in different agricultural commodities varies across different categories and production systems [Fig 5.1]. In general, meat products have very large water requirement due to feed crops, drinking water and service water required before its meat is consumed. For instance, the virtual water content in beef is about 9 times the amount in paddy rice and 12 times the amount in maize.

18,000 16,000 14,000 12,000

10,000

/ton 3

m 8,000 6,000 4,000 2,000 0 Beef Pork Hen egg Poultry Wheat Oats Paddy Barley Maize rice

Green Blue Grey

Figure 5.1: Global average virtual water footprint of agricultural products Data source: Mekonnen and Hoekstra (2010)

The water footprint is largely dependent on the climatic conditions and water use efficiency practice in food-producing countries. The crop water requirement per unit of crop produced is relatively high in regions with high evaporative demand. This can be partly explained by the high virtual water values in countries like South Africa. High water footprints can also be explained by the poor agricultural water use efficiency. In

Thailand, the average crop yield of rice is 2.94 ton ha-1, lower than that of Vietnam &

India (5.02 ton ha-1 & 3.18 ton ha-1) and less than half of world producer for rice,

China (6.38 ton ha-1). On the other hand, Australia has the highest rice yield of 8.72 ton ha-1 [Fig. 5.2]. The virtual water content of rice for China, Australia & Thailand are 1010 m3 ton-1, 1403 m3 ton-1 & 2286 m3 ton-1 respectively [Appendix F.1].

10 8.72 7.70 8 6.38

6 5.02 4.73

ton/ha 3.50 4 3.18 2.94

0 Australia USA China Viet Nam Indonesia Malaysia India Thailand

Figure 5.2: Crop yield of rice in various countries Data source: FAO (2015)

3,500 3,000

2,500

2,000 /ton

3 1,500 m 1,000 500 0 Australia USA China Viet Nam Indonesia Malaysia India Thailand

Green Blue Grey

Figure 5.3: Average virtual water footprint of paddy rice Data source: Mekonnen and Hoekstra (2010)

5.2 Virtual water content of live animals by farming systems

Table 5.1 shows the global average virtual water content of selected live animals for each of the 3 farming systems. In general, livestock produced using grazing system has higher virtual water content compared to mixed and industrial systems due to longer lifetime of livestock and lower stock yield. Besides the physical characteristics of the farming system, climate, crop productivity, irrigation and fertiliser use also affect the water footprint in animal feed and hence, the water footprint of overall production.

Mixed cattle system is the dominant system for example in Brazil, China, Ethiopia,

India, New Zealand and the US. For cattle, industrial systems are the dominant farming system in for example Japan and western European countries. For swine and poultry meat, industrial system has become the main system for most parts of the world.

Table 5.1: Global average virtual water content of live animals per farming system

Farming systems Unit Beef cattle Swine Sheep Poultry Grazing system m3/ton 21,829 8,724 16,311 7299 Mixed system m3/ton 15,712 6,226 8,335 3880 Industrial system m3/ton 10,244 5,225 5,623 2231 Weighted average m3/ton 15,415 5,988 10,412 3,364 Data source: Mekonnen and Hoekstra (2010)

25,000

20,000

15,000

/ton 3

m 10,000 5,000 0 Beef cattle Swine Sheep Poultry

Grazing system Mixed system Industrial system

Figure 5.4: Global average virtual water content of live animals per farming system Data source: Mekonnen and Hoekstra (2010)

In general, the virtual water content of beef cattle is relatively high compared to other livestock meat. Beef requires an average 15,712 m3/ton of water; sheep meat 8,335 m3/ton; pork takes an average of 5,225 m3/ton; poultry meat 2,231 m3/ton at the end of life. The virtual water values vary across different countries due to climate, agricultural productivity and different farming practices [Gerbens-Leenes et al., 2011].

Table 5.2 shows the average annual water footprint of one animal per animal category

(m3/yr/animal). It should be noted that the average annual virtual water footprint of one animal refers to the virtual water content of each animal at the end of their average life

24 span or their slaughter age. This is shorter than the natural lifetime of the animal. The average animal weight at end of life will be used to compute the annual quantity

(tonnes) of live animals imported.

Table 5.2: Average annual water footprint of one animal per animal category

Animal Water Average Average water Average Average annual category footprint of animal footprint at life time water footprint live animal weight at end of life (yr) of one animal at end of end of life (m3/animal) (m3/yr/animal) life time time (kg) (m3/ton) Beef cattle 15,415 253 3,900 3 1300 Pig 5,988 102 611 0.75 814 Sheep 10,412 31.3 326 2.1 155 Broiler 3,364 1.9 6 0.25 26 chicken Data source: Mekonnen and Hoekstra (2010)

CHAPTER 6 – SINGAPORE’S VIRTUAL WATER IMPORT FLOWS

6.1 Comparison of results with earlier study

Singapore gross virtual water volumes imported for the period related to trade in crops and livestock was 3,473 million m3 per year. Table 6.1 compares the results calculated with D. Vanham’s (2011) study. The discrepancies in values reported are mainly due to different study period and virtual water content estimates used. Overall, crop products show a higher proportion of virtual water volumes imported.

Table 6.1: Comparison of results with earlier study (D. Vanham, 2011)

Items D. Vanham, 2011 Present study (1997 – 2001) (2002 – 2011) 6 3 6 3 10 m /yr 10 m /yr Rice 614 895 Wheat 214 343

Net Virtual Water Import Crop products 2,386 1946 Livestock products 1,461 1527 Industrial products 7,934 - Total (exclude industrial products) 3,847 3,473

6.2 Virtual water import flows per year 1,000 900 800 700

600

/yr 3

m 500

10 400 300 200 100 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Thailand Malaysia Brazil Australia USA

Figure 6.1: Virtual water import flows by countries (time – series)

Figure 6.1 shows the annual fluctuation of virtual water import flows from major food

– producing countries. Although the gross total virtual water consumption did not fluctuate excessively in the last decade (see appendix G.1), Singapore is still vulnerable to food unavailability due climate variability in food-producing countries.

6.3 Virtual water import flows per product

The virtual water trade by food commodities is illustrated in Figure 6.2. Livestock products contributed to the largest percentage of gross total virtual water consumption in Singapore (44%), followed by cereals (37%), fruits and vegetables (16%), roots & tubers (3%). The high proportion of virtual water import flows from livestock is due to shift in food consumption pattern towards animal products which are generally water – intensive [Table 6.2].

Figure 6.2: Contribution of various food commodities to the gross total virtual water consumption in Singapore

Table 6.2: Contribution of various food commodities to the gross total virtual water consumption in Singapore

Food Items Mm3/yr Share (%) Livestock 1,527.19 43.98 Chicken 480.87 13.85 Pork 402.61 11.59 Beef, cattle 326.77 9.41 Hen eggs 167.52 4.82 Sheep 61.48 1.77 Goat 53.35 1.54 Duck 34.59 1.00

Cereals 1,293.54 37.25 Rice 894.58 25.76 Wheat 342.56 9.86 Maize 31.32 0.90 Cereal unmilled 19.77 0.57 Oats rolled 4.59 0.13 Oats 0.45 0.01 Barley 0.27 0.01

Fruits 393.15 11.32 Vegetables 150.50 4.33 Roots and tubers 108.42 3.12 Total 3,472.80 100.00

6.4 Virtual water import flows per country

Table 6.3: Annual virtual water import flows related to agricultural products from major food producing countries

No. Countries Unit Total % Share Cum. % 1. Thailand Mm3/yr 778 22.40 22.40 2. Malaysia Mm3/yr 691 19.89 42.28 3. Brazil Mm3/yr 586 16.88 59.16 4. Australia Mm3/yr 412 11.86 71.02 5. USA Mm3/yr 217 6.24 77.26 6. India Mm3/yr 168 4.83 82.09 Others Mm3/yr 622 17.91 100.00

Total Mm3/yr 3,473 100.00

The six major food producing countries that contributed largely to the gross total virtual water consumption related to agricultural products in Singapore are Thailand

(778 Mm3/yr), Malaysia (691 Mm3/yr), Brazil (586 Mm3/yr), Australia (412 Mm3/yr),

USA (217 Mm3/yr) and India (168 Mm3/yr) [Table 6.3]. Thailand contributed to the largest gross virtual water consumed (22.40%) due to high virtual water content in rice

(2,286 m3/ton) and import quantity (186,658 ton/yr). The food composition of the 10 food producing countries in relation to the gross total virtual water consumption in

Singapore during the period is shown in Figure 6.3.

900

800

700

600

/yr 500

6 400 10

300

200

100

0 Thailand Malaysia Brazil Australia USA India China Viet Nam Indonesia New Zealand Countries

Cereals Roots Fruits Livestock Vegetables

Figure 6.3: Food composition of virtual water import flows per country

CHAPTER 7 – AGRICULTURE AND WATER RESOURCES

This section evaluates the degree of freshwater resources vulnerability in selected food-producing countries using various indices such as Falkenmark water stress index and water poverty index. The study will link water and agriculture and assess the implications of freshwater resources vulnerability on food security in Singapore.

7.1 Global distribution of freshwater resources

Over 60 % of the world’s freshwater supply is found in just 9 countries and agriculture accounted for 70% of global freshwater withdrawals. However, this share is expected to decline as the proportion of withdrawals from industrial activities continues to expand. The countries which possess more than 60% of global freshwater supply include Brazil (13%), Russia (10%), Canada (7%), USA (7%) and China (6%). The uneven global distribution of freshwater resources is even more apparent on per capita availability basis (Falkenmark water stress index).

Figure 7.1: Breakdown of global freshwater withdrawals and global freshwater resources distribution. Data source: World Bank, 2013 data.

7.2 Falkenmark water stress index

More abundant Less abundant 90,000

80,000

70,000

60,000

50,000

/cap/yr 3

m 40,000

30,000

20,000

10,000

USA

India

China

Brazil

France

Canada

Pakistan

Uruguay

Thailand

Malaysia

Australia

Denmark

Myanmar

Indonesia

Viet Nam Viet

Argentina

Singapore

Philippines

Netherlands South Africa South New Zealand New Figure 7.2: Per capita freshwater availability of various food producing countries

For example, Canada population is just 11% of United States but both possess 7% of the world’s freshwater supply. Canada per capita water resources available is 82,485 m3 per person in contrast to just 9,589 m3 per person in United States [Fig. 7.2]. This resulted in wide disparity per capita distribution between water – scarce and water- abundant nations. Below the threshold of 1,700m³ per capita, water scarcity occurs in different levels of severity for countries including India, Pakistan, Denmark, South

Africa and Singapore [Fig. 7.3].

Figure 7.3: Logarithmic diagram showing water demand and water availability of various food-producing countries. The horizontal axis shows per capita water availability and vertical axis shows water demand expressed in m3 per person per year. Crossing lines show different mobilization levels of water availability.

7.3 Virtual water flows related to trade in agricultural products

5,000 4,500 4,000

3,500

3,000

2,500

/cap/yr 3

m 2,000 1,500 1,000 500

USA

India

China

Brazil

France

Canada

Pakistan

Uruguay

Thailand

Australia Malaysia

Denmark

Myanmar

Indonesia

Viet Nam Viet

Argentina

Singapore

Philippines

Netherlands

South Africa South New Zealand New

GVWI GVWE

Figure 7.4: Virtual – water flows related to trade in agricultural products per capita

The abundant of freshwater resources in a few countries are evident by comparing

Falkenmark water stress index with virtual water flows related to trade in agricultural products of the countries [Fig.7.4]. Intuitively, countries with high per capita water availability are net virtual water exporters of the world. There are a few notable exceptions: India and Pakistan which are high on water stress are also net virtual water exporters, indicating unsustainable water use [Appendix H].

Table 7.1: Top 5 gross virtual water importers and exporters related to food and agricultural products

Ranking Country GVWI Ranking Country GVWE (Gm3/yr) (Gm3/yr) 1 USA 184 1 USA 289 2 China 108 2 India 114 3 France 67 3 Brazil 110 4 Netherlands 64 4 Argentina 98 5 Malaysia 38 5 China 92 Data source: Mekonnen and Hoekstra (2011). Period: 1996 – 2005.

However, all countries experience different degree of water stress using the Water

Scarcity Index [Appendix I]. Among them, USA (289 Gm3/yr), India (114 Gm3/yr),

Brazil (110 Gm3/yr), Argentina (98 Gm3/yr), China (92 Gm3/yr), Australia (88 Gm3/yr),

Canada (82 Gm3/yr), Indonesia (72 Gm3/yr), Pakistan (60 Gm3/yr) and France (58

Gm3/yr), are the largest gross virtual water exporters related to agricultural products in the world [Appendix K]. They accounted for 49.75% of Singapore’s virtual water import volumes [Appendix G.3]. The severity of water stress is especially high in India and Pakistan, and agriculture accounted for more than 90% of their annual freshwater withdrawals [Appendix J.5].

7.4 Water poverty index

The WPI values of various selected countries are presented in Figure 7.5. The 5 ‘most water sustainable’ countries by ranking are Netherlands (70.06), Canada (67.60), New

Zealand (64.66), Australia (63.89) and France (60.83) [Table 7.2]. Interestingly,

Netherlands ranks the highest although per capita internal renewable water resources of the country is only 656 m3 compared to Canada (81,007 m3), New Zealand (72,570 m3), Australia (21,077 m3) and France (3,111 m3). Netherlands ranked highly relative to the others in term of access to improved water sources and sanitation facilities

(100%), percentage of arable land equipped for irrigation (48%), UNDP education index (0.89), low mortality rate (4 per 1,000 live births) and high GDP per capita, PPP

US$ 41,980 [Appendix J].

Netherlands Canada New Zealand Australia France Malaysia United States Uruguay Thailand Denmark Argentina Vietnam Brazil Singapore Myanmar China Indonesia Philippines Pakistan South Africa India 0 10 20 30 40 50 60 70 80

Use Access Capacity Environment Resources

Figure 7.5: Water Poverty Index of various selected countries

Table 7.2: The Water Poverty Index and sub-components

Country Name Use Access Capacity Environment Resources Total Netherlands 11.19 16.46 18.31 14.06 10.03 70.06 Canada -* 13.22 17.21 17.17 20.00 67.60 New Zealand 1.52 20.00 17.60 5.91 19.64 64.66 Australia 2.17 13.58 17.97 14.28 15.89 63.89 France 4.32 14.16 16.95 15.21 10.18 60.83 Malaysia 3.45 15.27 12.66 13.74 15.66 60.78 United States 0.10 14.01 16.86 15.75 13.38 60.10 Uruguay 5.78 13.53 13.20 9.47 17.74 59.73 Thailand 8.31 13.38 11.86 13.03 11.50 58.08 Denmark 5.77 14.41 18.39 12.52 6.86 57.95 Argentina 2.77 12.75 14.51 12.21 14.68 56.91 Vietnam 7.76 13.26 9.07 13.63 12.46 56.18 Brazil 4.58 10.66 10.63 11.60 17.49 54.96 Singapore 5.84 20.00 18.09 10.00 0.00 53.94 Myanmar 14.95 5.98 4.23 11.96 15.79 52.92 China 6.40 10.33 11.91 14.36 8.75 51.75 Indonesia 7.61 4.18 10.27 14.92 12.99 49.97 Philippines 5.92 8.87 8.98 13.10 11.46 48.33 Pakistan 12.83 10.50 4.65 13.02 6.09 47.09 South Africa 5.77 9.27 7.16 16.67 6.38 45.25 India 9.55 6.16 7.27 13.08 7.55 43.61 *missing data

New Zealand & USA are relatively well endowed with freshwater resources; they are also net virtual water exporters for food. Despite their relatively high scores on access, capacity and resources, the low values on use reflect poor water use efficiency in their countries [Table 7.2]. Agriculture is a major freshwater withdrawal in New Zealand

(74%) and USA (40%), yet the sector only value added 7.2% & 1.3% to their GDP respectively. In addition, per capita domestic freshwater withdrawal is high in both countries (629 & 567 l/p/d respectively). The low water use efficiency for agriculture and high per capita domestic freshwater withdrawal ranked them low on use, suggesting weakness in their freshwater system management [Appendix J.5].

The 10 ‘most water sustainable’ countries by ranking accounted for 64.37 % of

Singapore’s gross virtual water import volumes in the period. More importantly, the 5

‘most water sustainable’ countries on the list only accounted for 15.40 % of

Singapore’s gross total virtual water consumption, implying relatively unsustainable virtual water trade pattern and consumption of the country [Appendix G.3].

Figure 7.6: Composition of virtual water import flows for the ‘most and least water sustainable’ countries. The 5 ‘most water sustainable’ countries are Netherlands, Canada, New Zealand, Australia and France. The 5 ‘least water sustainable’ countries are India, South Africa, Pakistan, Philippines and Indonesia.

Among the 5 ‘most water sustainable’ countries, livestock accounted for nearly half of gross total virtual water consumption mainly due to meat imports from Australia and

New Zealand. However, Netherlands, Canada and France in total only accounted for

2.3% of gross total virtual water consumption despite high standing in water sustainability. Netherlands is the world's 2nd largest agricultural exporter, after the

United States and 4th largest meat exporter after US, Brazil and Germany by trade value (Ministry of Economic Affairs, 2015). Netherlands agriculture sector value added 2% of their GDP yet the sector only use 0.67% of total freshwater withdrawal.

The high agricultural water use efficiency gives them comparative advantages over other countries in terms of water use efficiency and productivity.

CHAPER 8 – DISCUSSION

8.1 Study implications

Virtual water trade improves food security, freshwater availability and livelihoods for water-scarce countries like Singapore. In the last few decades, Singapore dietary preferences shifted from cereal grains to meat consumption. It is unlikely that this trend will vary by much in the next few decades. Singapore will need to continue engage with water-rich countries for food security.

The study analyzed how food consumption patterns can affect virtual water demand, and how changes in future food consumption patterns are likely to have an impact on water use. The assessment on recent food trade and virtual water trade provides a comprehensive assessment on consumption-based virtual water indication.

Figure 8.1 shows the composition of virtual water import flows under different water resources conditions during the period. The sustainability of water resources depends on the impact of water use. The various water resources indices can be used as a tool to influence towards sustainable production and consumption in Singapore. The results show that as the criteria to assess water resources vulnerability become more stringent across different indices, the composition of virtual water import flows under different water conditions vary significantly.

Figure 8.1: Composition of virtual water import flows under different water conditions

8.2 Limitations of the study

Water management plays a critical role in the sustainability of agriculture, especially in water – scarce regions. The Falkenmark water stress index and water scarcity index are common indices used to measure water stress on a national scale. The indicators assume water availability within a country is evenly distributed and change in demand for water due to climatic, physiographic and socio-economic factors are not considered.

Thus, both indicators do not give a complete picture of water scarcity.

Conversely, the water poverty index is a holistic approach used to assess the water situation, especially in arid and semi – arid regions. WPI covers a wider aspect of freshwater availability and use. However, it is complex to use due to the amount of information required. In addition, the relative position also depends on the selected countries. More countries can be included in the calculation to improve the results.

WPI gives sensible results in freshwater sustainability of a country but the indicator is by no means to give an explicit condition of a country water resources and use. While

39 no method can be a perfect model to measure sustainability, WPI can provides a means to understand the complexity of water issues by incorporating physical, socio- economic and environmental aspects. The sub-indices are by no means exhaustive, diverse and relevant aspects related to freshwater management can be introduced depending on the utility.

It should be noted that some countries’ position may be affected by missing data or poor performance in certain aspects which resulted in lower standing. A country may show strengths in other aspects not covered in the index. For example, Singapore does not have water and land resources for agriculture production. Thus, the country ranked the worst relative to the others in the resources component. The country depends on imported and recycled water for domestic water supply. On the other hand,

Netherlands is the second largest exporter of food and agricultural products, behind

United States. Yet, the country is an overall net virtual water importer related to agricultural products. This suggests that a country still can improve water efficiency by importing less water efficient crops from more water efficient ones to optimize their water resources.

CHAPTER 9 – RECOMMENDATIONS AND CONCLUSIONS

9.1 Recommendations

The study focused on data for the period between 2002 and 2011 which include trade in 49 primary crops and trade in 7 livestock products. The gross total virtual water consumption of the various agricultural products is calculated using statistical data and data from literature review. The reliability of the results can be improved by providing a comprehensive estimate of virtual water flows. This can be achieved by analyzing additional items such as processed food items. However, it is unlikely that the overall compositions will change by much since 90% of the virtual water import flows analyzed are the major food items commonly consumed in Singapore. In addition, high consumption of industrial commodities also contributed significantly to the overall virtual water footprints (D. Vanham, 2011).

The virtual water assessment is an indicator of freshwater use due to food consumption volumes and patterns. The aggregated values quantify the total water required and measure the impact of human consumption on the freshwater resources worldwide. A more balanced assessment on the effects of freshwater systems is to look at what is blue versus green water use as the blue water use has a greater impact on global freshwater resources. Shift in dietary preferences for more water – intensive meat products will result in intensification of production from grazing and mixed to industrial system. This would increase the overall water footprint of production as a result.

Climate change affects crop production through direct impacts on the biophysical factors such as plant and animal growth and the physical infrastructure associated with food processing and distribution (Schmidhuber and Tubiello, 2007). The crop water requirement per unit of crop produced is relatively high in regions with high evaporative demand. Nearly 50 % of crop yield is attributed to the influence of climatic factors.

Recent research has suggested that some impacts of climate change are occurring more rapidly than previously anticipated (Parry et al., 2007). Crop production is directly affected by many aspects of climatic change, stemming primarily from average temperature increase, change in rainfall amount and patterns, rising atmospheric concentration of CO2, change in climatic variability and extreme events.

Climate change has significant negative effects on the crop yields. A lower agricultural production and productivity due to climatic change has implications on food prices, which in turn affect the livelihood and food security of a country. Thus, it is essential to review the impacts of climatic change on food crop productivity and food accessibility of food – producing countries and Singapore respectively.

9.2 Conclusions

In the last few decades, Singapore dietary preferences shifted from cereal grains to

meat consumption. It is unlikely this trend will vary by much in the next few

decades. Singapore will need to continue engage with water-rich countries for food

security. The food import quantity during the period 2002 – 2011 related to trade in

crops and livestock products was 1,670,074 ton/yr. Vegetables form the largest

share of food items imported by quantity (24%), followed by fruits (19%), rice

(18%), chicken & wheat (10 % each). Malaysia (484,397 ton/yr), Thailand

(266,482 ton/yr), China (196,686 ton/yr), Australia (164,135 ton/yr), USA

(136,291 ton/yr) and Brazil (98,022 ton/yr) contributed to 80% of annual food

quantity imported.

Singapore gross virtual water volumes imported for the period related to trade in

crops and livestock was 3,473 million m3 per year or 2,078 litres per capita daily

consumption. Livestock products contributed to the largest percentage of gross

total virtual water consumption in Singapore (44%), followed by cereals (37%),

fruits and vegetables (16%), roots and tubers (3%). The high proportion of virtual

water import flows from livestock is due to the shift in food consumption towards

animal products which are generally water – intensive. The six major food

producing countries that contributed to 80% of GVWI flows in Singapore are

Thailand (778 Mm3/yr), Malaysia (691 Mm3/yr), Brazil (586 Mm3/yr), Australia

(412 Mm3/yr), USA (217 Mm3/yr) and India (168 Mm3/yr).

The study examines whether virtual water consumption related to trade in crop and livestock products are sustainable in Singapore. A sustainable country should account for its impacts beyond its borders. The ability of global freshwater resources to meet future food demand is highly dependent on consumption patterns within the country. The average virtual water consumption is more than 10 times the average domestic water consumption of 150 litres per capita daily (PUB, 2014).

Although the water supply system of Singapore is highly regarded as a role model for other countries, the virtual water consumption of an average citizen within the country is not sustainable.

Despite the physical or monetary saving for Singapore to import products from more water-efficient countries may not be significant, virtual water is still an important topic that can help inform public policy. Consumption behaviour has a determining influence on virtual water trade and every individual’s commitment counts towards protecting the global freshwater resources.

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APPENDICES

Appendix A – Crops and livestock products included in this study

A.1 Primary crops

No. Names Cereals 1. Wheat 2. Rice (husked, milled, broken) 3. Cereals unmilled 4. Barley 5. Maize 6. Oats 7. Oats rolled

Vegetables 8. Cabbages and other brassicas 9. Asparagus 10. Lettuce and chicory 11. Spinach 12. Tomatoes 13. Cauliflowers and broccoli 14. Cucumbers and gherkins 15. Eggplants (aubergines) 16. Chillies and peppers, green 17. Onions, dry 18. Garlic 19. Beans, green 20. Peas, green 21. Carrots and turnips 22. Vegetables, fresh 23. Watermelons 24. Melons, other (inc.cantaloupes)

Fruits 25. Bananas 26. Oranges 27. Tangerines, mandarins, clementines, satsumas 28. Lemons and limes 29. Grapefruit (inc. pomelos) 30. Apples 31. Pears 32. Apricots 33. Cherries 34. Peaches and nectarines 35. Plums and sloes 36. Strawberries 37. Grapes 38. Figs dried

39. Mangoes, mangosteens, guavas 40. Avocados 41. Pineapples 42. Dates 43. Kiwi fruit 44. Papayas 45. Fruit, tropical fresh

Roots and tubers 46. Potatoes 47. Sweet potatoes 48. Starch, cassava 49. Roots and tubers

A.2 Livestock products

No. Names Descriptions

Beef Meats are of live, chilled & frozen 50. Meat, cattle forms. 51. Meat, cattle, boneless (beef & veal) 52. Live cattle

Mutton 53. Meat, goat 54. Live goat 55. Meat, sheep 56. Live sheep

Poultry meat 57. Meat, chicken 58. Live chicken 59. Meat, duck 60. Live duck

Pork 61. Meat, pork 62. Live pig

63. Hen eggs Hen shell eggs

Appendix B – Singapore annual population and growth rate

Year Total Population Annual growth (Per Cent) 1960 1,646,400 1961 1,702,400 3.40% 1962 1,750,200 2.81% 1963 1,795,000 2.56% 1964 1,841,600 2.60% 1965 1,886,900 2.46% 1966 1,934,400 2.52% 1967 1,977,600 2.23% 1968 2,012,000 1.74% 1969 2,042,500 1.52% 1970 (Census) 2,074,507 1.57% 1971 2,112,900 1.85% 1972 2,152,400 1.87% 1973 2,193,000 1.89% 1974 2,229,800 1.68% 1975 2,262,600 1.47% 1976 2,293,300 1.36% 1977 2,325,300 1.40% 1978 2,353,600 1.22% 1979 2,383,500 1.27% 1980 (Census) 2,413,945 1.28% 1981 2,532,835 4.93% 1982 2,646,466 4.49% 1983 2,681,061 1.31% 1984 2,732,221 1.91% 1985 2,735,957 0.14% 1986 2,733,373 -0.09% 1987 2,774,789 1.52% 1988 2,846,108 2.57% 1989 2,930,901 2.98% 1990 (Census) 3,047,132 3.97% 1991 3,135,083 2.89% 1992 3,230,698 3.05% 1993 3,313,471 2.56% 1994 3,419,048 3.19% 1995 3,524,506 3.08% 1996 3,670,704 4.15% 1997 3,796,038 3.41% 1998 3,927,213 3.46% 1999 3,958,723 0.80% 2000 (Census) 4,027,887 1.75% 2001 4,138,012 2.73% 2002 4,175,950 0.92% 2003 4,114,826 -1.46% 2004 4,166,664 1.26%

2005 4,265,762 2.38% 2006 4,401,365 3.18% 2007 4,588,599 4.25% 2008 4,839,396 5.47% 2009 4,987,573 3.06% 2010 (Census) 5,076,732 1.79% 2011 5,183,688 2.11% 2012 5,312,437 2.48% 2013 5,399,162 1.63% 2014 5,469,724 1.31% Data source: Singapore Department of Statistics (2014)

Appendix C – Quantity of main food items traded in Singapore

C.1 Food Imports

Food Items Unit 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Chicken ton 151,356 159,487 126,672 140,644 134,342 156,913 163,729 160,715 169,163 180,177 183,343 182,123 Pork ton 75,925 83,267 88,446 84,187 88,356 95,414 95,686 97,173 104,276 100,882 110,744 104,554 Vegetables ton 359,348 355,046 409,664 407,908 421,887 431,585 444,467 468,160 480,882 487,505 501,412 514,574 -Leafy ton - - 76,110 74,585 72,738 72,578 74,291 77,845 78,138 77,864 81,982 80,544 Vegetables -Other ton - - 333,553 333,322 349,149 359,008 370,177 390,315 402,744 409,641 419,430 434,031 Vegetables Hen eggs 106 819 874 739 876 930 1,014 1,126 1,165 1,241 1,210 1,237 1,248 pieces Fruits ton 423,562 393,107 402,113 394,164 384,782 365,226 368,243 372,075 368,594 374,091 388,364 414,774 Beef ton 15,697 19,545 17,581 18,110 24,278 25,848 26,200 23,631 25,485 27,785 26,718 26,212 Duck ton 16,766 15,721 10,788 14,839 12,643 14,759 15,047 13,562 14,551 13,711 13,647 13,657 Mutton ton 8,480 8,556 9,471 8,919 9,959 10,719 11,006 11,636 10,441 10,060 10,548 12,331 Data source: AVA, Singapore

C.2 Food Exports

Food Items Unit 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Chicken ton - - 15,973 12,130 16,368 14,453 8,030 7,008 7,703 9,862 9,822 10,070 Pork ton - - 1,379 1,075 761 944 516 865 902 2,060 3,018 10,743 Vegetables ton - - 44,862 29,051 28,650 24,672 20,974 36,307 30,488 23,298 21,292 36,160 -Leafy Vegetables ton - - 3,655 4,059 3,720 3,742 3,891 3,924 6,267 7,217 5,780 5,274 - Other Vegetables ton - - 41,207 24,992 24,930 20,930 17,083 32,383 24,221 16,081 15,512 30,886 Hen eggs 106 pieces - - 0.1 0.9 2.2 3.7 1.1 0.6 0.5 0.2 1 2 Fruits ton - - 41,519 33,440 32,335 26,556 22,862 20,438 23,851 24,933 32,126 36,868 Beef ton - - 4,195 3,097 9,711 8,705 7,283 5,870 6,443 8,775 8,527 13,090 Duck ton - - 123 143 32 74 124 137 142 85 38 46 Mutton ton - - 306 297 298 271 167 119 347 412 283 211 Data source: AVA, Singapore

Appendix D – Per capita consumption of main food items in Singapore

Item Unit 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Chicken kg cap-1 yr-1 35.9 37.4 27 30 27 31 32 31 32 33 33 32 Pork kg cap-1 yr-1 18.2 19.8 21 19 20 21 20 19 20 19 20 17 Vegetables kg cap-1 yr-1 83.8 83.4 92 93 93 93 91 91 93 94 94 93 - Leafy vegetables kg cap-1 yr-1 - - 19 18 18 17 16 17 16 16 16 16 - Other vegetables kg cap-1 yr-1 - - 72 74 76 76 75 74 77 78 78 77 Hen eggs pieces cap-1 yr-1 282.5 302.3 268 286 291 302 302 300 311 307 308 312 Fruits kg cap-1 yr-1 87.1 82.3 87 85 80 74 71 71 68 67 67 70 Beef kg cap-1 yr-1 3.7 4.6 3 4 3 4 4 4 4 4 3 2 Duck kg cap-1 yr-1 4 3.7 3 3 3 3 3 3 3 3 3 3 Mutton kg cap-1 yr-1 2 2 2 2 2 2 2 2 2 2 2 2 Data source: AVA, Singapore

Appendix E – Annual food import quantity

E.1 Food classification

Year Unit Cereals Vegetables Fruits Roots Livestock Total 2002 m3/yr 615,350 386,565 320,455 85,555 251,476 1,659,401 2003 m3/yr 521,035 366,324 322,797 91,257 284,944 1,586,357 2004 m3/yr 479,661 371,939 311,818 92,065 285,609 1,541,092 2005 m3/yr 422,618 384,256 307,935 140,926 343,549 1,599,284 2006 m3/yr 447,688 399,584 301,845 104,904 344,303 1,598,324 2007 m3/yr 500,246 397,937 296,359 103,062 390,060 1,687,664 2008 m3/yr 471,575 401,921 309,837 105,877 410,614 1,699,824 2009 m3/yr 472,384 426,845 308,674 116,045 404,414 1,728,362 2010 m3/yr 509,027 440,353 303,838 113,991 419,546 1,786,755 2011 m3/yr 551,287 441,395 311,852 119,337 389,811 1,813,682 Average m3/yr 499,087 401,712 309,541 107,302 352,432 1,670,074 Data source: FAO (2015)

E.2 Food items

No. Food Items ton/yr % Share Cereals 1 Wheat 166,104 9.95% 2 Rice 303,314 18.16% 3 Cereal unmilled 7,292 0.44% 4 Barley 396 0.02% 5 Maize 19,914 1.19% 6 Oats 222 0.01% 7 Oats rolled 1,845 0.11% Sub-total 499,087 29.88%

Vegetables 8 Cabbages and other brassicas 53,265 3.19% 9 Asparagus 898 0.05% 10 Lettuce and chicory 12,759 0.76% 11 Spinach 9,472 0.57% 12 Tomatoes 24,968 1.50% 13 Cauliflowers and broccoli 13,584 0.81% 14 Cucumbers and gherkins 23,230 1.39% 15 Eggplants (aubergines) 7,654 0.46% 16 Chillies and peppers, green 13,244 0.79% 17 Onions, dry 45,666 2.73% 18 Garlic 12,002 0.72% 19 Beans, green 11,004 0.66% 20 Peas, green 660 0.04% 21 Carrots and turnips 22,190 1.33% 22 Vegetables, fresh 36,415 2.18% 23 Watermelons 14,004 0.84% 24 Melons, other (inc.cantaloupes) 100,698 6.03% Sub-total 401,712 24.05%

Fruits 25 Bananas 37,549 2.25% 26 Oranges 40,259 2.41% 27 Tangerines, mandarins, clementines, satsumas 17,880 1.07% 28 Lemons and limes 6,519 0.39% 29 Grapefruit (inc. pomelos) 3,870 0.23% 30 Apples 45,015 2.70% 31 Pears 24,788 1.48% 32 Apricots 63 0.00% 33 Cherries 826 0.05% 34 Peaches and nectarines 1,700 0.10% 35 Plums and sloes 3,152 0.19% 36 Strawberries 2,064 0.12% 37 Grapes 12,298 0.74% 38 Figs dried 177 0.01%

39 Mangoes, mangosteens, guavas 17,527 1.05% 40 Avocados 662 0.04% 41 Pineapples 17,237 1.03% 42 Dates 1,765 0.11% 43 Kiwi fruit 2,769 0.17% 44 Papayas 23,615 1.41% 45 Fruit, tropical fresh 49,807 2.98% Sub-total 309,541 18.53%

Roots and tubers 46 Potatoes 42,062 2.52% 47 Sweet potatoes 7,021 0.42% 48 Starch, cassava 46,822 2.80% 49 Roots and tubers 11,396 0.68% Sub-total 107,302 6.42%

Livestock Beef 50 Meat, cattle 1,818 0.11% 51 Meat, cattle, boneless (beef & veal) 17,741 1.06% 52 Live cattle 24 0.00%

Mutton 53 Meat, goat 19,500 1.17% 54 Live goat 480 0.03% 55 Meat, sheep 8,942 0.54% 56 Live sheep 357 0.02%

Poultry meat 57 Meat, chicken 92,666 5.55% 58 Live chicken 79,784 4.78% 59 Meat, duck 912 0.05% 60 Live duck 11,543 0.69%

Pork 61 Meat, pork 41,980 2.51% 62 Live pig 17,972 1.08%

Eggs 63 Hen shell eggs 58,713 3.52% Sub-total 352,432 21.10%

Grand total 1,670,074 100.00% Data source: FAO (2015)

E.3 Food – producing countries

No. Countries Unit Cereals Vegetables Fruits Roots Livestock Total % Share Cum. % 1. Malaysia ton/yr 5250 221873 104651 4400 148223 484397 29.00% 29.00% 2. Thailand ton/yr 188404 9277 21193 47172 437 266482 15.96% 44.96% 3. China ton/yr 7159 89177 66772 25575 8003 196686 11.78% 56.74% 4. Australia ton/yr 84764 22123 17619 4026 35604 164135 9.83% 66.57% 5. USA ton/yr 64587 8603 32664 6988 23449 136291 8.16% 74.73% 6. Brazil ton/yr 98022 98022 5.87% 80.60% 7. Viet Nam ton/yr 62267 6374 5395 5328 494 79859 4.78% 85.38% 8. India ton/yr 54440 23114 500 786 78840 4.72% 90.10% 9. Indonesia ton/yr 295 11990 302 5953 17974 36514 2.19% 92.28% 10. Canada ton/yr 21038 55 2118 23212 1.39% 93.67% 11. South Africa ton/yr 80 22918 120 23118 1.38% 95.06% 12. Philippines ton/yr 608 621 16315 17544 1.05% 96.11% 13. New Zealand ton/yr 35 2915 7618 1750 5077 17395 1.04% 97.15% 14. Netherlands ton/yr 357 4406 6 1048 5469 11286 0.68% 97.83% 15. Myanmar ton/yr 8409 8409 0.50% 98.33% 16. France ton/yr 2896 5 3402 6304 0.38% 98.71% 17. Pakistan ton/yr 850 2534 1020 4405 0.26% 98.97% 18. Bangladesh ton/yr 80 2950 3030 0.18% 99.15% 19. Argentina ton/yr 1306 1680 2985 0.18% 99.33% 20. Egypt ton/yr 2262 2262 0.14% 99.47% 21. Chile ton/yr 1259 1259 0.08% 99.54% 22. Japan ton/yr 872 298 14 1185 0.07% 99.61% 23. Denmark ton/yr 943 943 0.06% 99.67% 24. Italy ton/yr 892 0.40 893 0.05% 99.72% 25. Uruguay ton/yr 787 787 0.05% 99.77% 26. Turkey ton/yr 0.10 759 1 760 0.05% 99.82%

27. Korea ton/yr 91 398 128 617 0.04% 99.85% 28. Iran ton/yr 561 561 0.03% 99.89% 29. Spain ton/yr 390 120 2 512 0.03% 99.92% 30. Belgium ton/yr 455 455 0.03% 99.94% 31. Israel ton/yr 433 433 0.03% 99.97% 32. United Kingdom ton/yr 241 241 0.01% 99.98% 33. Germany ton/yr 107 25 132 0.01% 99.99% 34. United Arab Emirates ton/yr 1 91 92 0.01% 100.00% 35. Mexico ton/yr 25 25 0.00% 100.00% 36. Hungary ton/yr 3 3 0.00% 100.00% 37. Finland ton/yr 2 2 0.00% 100.00% 38. Ireland ton/yr 0.10 0.10 0.00% 100.00% Total ton/yr 499,087 401,712 309,540 107,302 352,432 1,670,074 100.00% Data source: FAO (2015)

Appendix F – Virtual water content estimates of agricultural products

F.1 Primary crops

Global

Items average

(m3/ton)

Australia Brazil Canada China India Indonesia Malaysia Zealand New Pakistan Philippines SouthAfrica Thailand USA Nam Viet Cereals

Wheat 1827 2116 2125 1542 1597 2100 - - 899 2533 - 1368 4174 2191 - Rice, Paddy 1673 1403 2403 - 1010 2070 1852 3104 - 3135 2313 4273 2286 1455 1391 Rice, husked 2172 1822 3120 - 1312 2688 2406 4031 - 4071 3004 5549 2969 1889 1807 Rice, milled 2414 2024 3467 - 1457 2986 2673 4479 - 4524 3338 6166 3299 2099 2008 Rice, broken 2497 2094 3586 - 1507 3089 2765 4633 - 4679 3452 6377 3412 2171 2077 Cereals unmilled 3441 - - - 1432 - - - 355 - - 2969 2218 - - Barley 1423 1794 2164 1014 726 2124 - - 763 5856 - 1509 2152 1302 - Maize 1222 1561 1746 647 1160 2537 1731 2607 752 2967 2149 1826 1039 762 1763 Oats 1788 2127 3816 1194 898 - - - 958 - - 3869 2123 - Oats rolled 2416 2875 5157 1614 1213 - - - 1295 - - 5229 2870 - Data source: Mekonnen and Hoekstra (2010)

Global

average

Items

(m3/ton)

Australia Brazil Canada China India Indonesia Malaysia Zealand New Pakistan Philippines SouthAfrica Thailand USA Nam Viet Vegetables

Cabbages and other brassicas 280 274 - 192 372 154 399 418 135 312 511 162 641 301 315 Asparagus 2150 2010 - 3583 2191 - - - 3316 - 1250 - 3203 2712 - Lettuce and chicory 237 187 - 147 293 645 - - 116 2115 - 341 346 113 - Spinach 292 254 - 332 294 - 1582 - 513 252 829 - - 184 - Tomatoes 214 143 94 55 284 226 525 291 58 569 519 116 285 127 - Cauliflowers and broccoli 285 355 - 244 307 268 317 - 66 147 305 256 825 326 222 Cucumbers and gherkins 353 603 - 92 387 439 502 231 - 365 731 488 668 423 - Eggplants (aubergines) 362 - - - 376 356 806 - - 317 563 - 1119 226 - Chillies and peppers, green 379 498 - 181 353 330 1222 - 185 - 1532 - 370 249 - Onions, dry 345 264 465 101 361 359 1108 - - 488 780 497 517 159 2310 Garlic 589 - 1148 - 512 903 1638 - 1377 753 2373 - 1357 379 - Beans, green 561 876 - 486 468 1286 681 - 329 257 809 699 1161 748 - Peas, green 595 516 - 800 711 537 - - 754 390 1406 1069 2621 525 - Carrots and turnips 195 288 - 67 290 197 378 - 77 203 408 245 - 124 - Vegetables, fresh 338 251 452 126 366 234 627 523 226 245 508 459 705 108 326 Watermelons 238 372 315 302 221 460 566 231 342 199 280 472 432 238 313 Melons, other (inc.cantaloupes) 221 217 237 134 228 214 277 - 320 167 369 417 - 182 - Data source: Mekonnen and Hoekstra (2010)

Global

Items average

(m3/ton)

Australia Brazil Canada China India Indonesia Malaysia Zealand New Pakistan Philippines SouthAfrica Thailand USA Nam Viet Fruits

Bananas 790 573 1003 - 630 502 1150 870 - 2457 1026 748 1480 762 957 Oranges 560 551 395 - 1371 881 698 1980 535 1081 3942 438 710 332 1248 Tangerines, mandarins etc. 748 559 515 - 993 - - - 986 1080 4848 461 704 455 - Lemons and limes 642 455 537 - 805 876 - 2388 766 1080 794 511 4040 349 - Grapefruit (inc. pomelos) 506 531 449 - 1099 384 - 1945 271 - 943 375 6903 303 810 Apples 822 708 370 394 1110 1925 - - 217 1196 - 477 - 447 - Pears 922 513 1120 649 1213 1476 - - 232 810 - 532 - 360 - Apricots 1287 1689 1187 1858 2926 - - 936 965 - 952 - 993 - Cherries 1604 2279 1409 2261 2865 - - 3306 5412 - 4018 - 1631 - Peaches and nectarines 910 1683 1169 627 1123 1644 - - 898 1453 - 1030 - 625 - Plums and sloes 2180 1384 1496 3255 2018 - - 1466 1099 - 1454 - 909 - Strawberries 347 516 926 728 659 - - - 165 - 534 801 - 165 - Grapes 608 524 352 524 563 329 - - 584 974 10417 422 555 416 - Figs dried 3276 4260 1212 - 2408 2058 - - - 2334 - 4099 - 1211 - Mangoes, mangosteens, guavas 1800 2629 933 - 1425 1909 2546 3841 - 1037 1956 1257 2861 3805 2324 Avocados 1173 2070 912 - 1048 - 1037 - 1755 - 1545 1865 1209 - Pineapples 255 123 160 - 272 367 131 212 - - 155 375 302 187 534 Dates 2277 - - - 649 - - - - 790 - - - 474 - Kiwi fruit 514 960 - 449 - - - - 406 - - - - 787 - Papayas 460 547 238 - 333 364 236 1340 - 1742 843 1144 1029 475 - Fruit, tropical fresh 1828 1281 950 - 2994 1390 1424 2318 1953 1261 1507 - 4294 - - Data source: Mekonnen and Hoekstra (2010)

Global

Items Average

(m3/ton)

Australia Brazil Canada China India Indonesia Malaysia Zealand New Pakistan Philippines SouthAfrica Thailand USA Nam Viet Roots and tubers

Potatoes 287 167 256 193 302 291 302 - 121 304 421 226 419 398 526 Sweet potatoes 383 312 454 - 301 666 618 484 277 483 1168 2460 - 890 757 Starch, cassava 2254 - 1804 - 1617 1125 1793 3097 - - 3883 - 1868 - 2418 Roots and tubers 385 - - - 272 - 627 676 - 203 721 - 313 - - Data source: Mekonnen and Hoekstra (2010)

F.2 Livestock products

F.2.1 Production system – Grazing

Country Unit Poultry Beef Pork Mutton Hen eggs

World Average m3/ton 7,299 21,829 8,724 16,311 7,644 Australia m3/ton 4,259 18,856 8,266 13,683 2,562 Brazil m3/ton 5,265 23,895 7,490 19,814 481 Canada m3/ton 2,913 20,455 6,756 11,428 2,218 China m3/ton 5,104 16,140 12,077 9,606 5,517 India m3/ton 11,611 25,913 4,449 11,441 13,140 Indonesia m3/ton 8,048 61,546 4,795 20,763 7,732 Malaysia m3/ton 7,125 80,679 3,398 14,042 6,286 New Zealand m3/ton 3,349 11,491 3,274 6,492 1,736 Pakistan m3/ton 12,267 42,398 - 24,560 12,147 Philippines m3/ton 8,173 53,093 7,046 24,242 9,156 South Africa m3/ton 5,977 23,621 8,547 14,233 6,702 Thailand m3/ton 6,857 64,653 8,592 20,913 7,350 USA m3/ton 2,819 20,217 6,878 12,240 2,254 Viet Nam m3/ton 8,060 51,056 7,047 - 6,462 Data source: Mekonnen and Hoekstra (2010)

F.2.2 Production system – Mixed

Country Unit Poultry Beef Pork Mutton Hen eggs

World Average m3/ton 3,880 15,712 6,226 8,335 3,863 Australia m3/ton 2,537 15,138 4,082 7,003 1,645 Brazil m3/ton 3,373 20,852 6,253 11,079 296 Canada m3/ton 1,737 13,396 7,162 9,826 1,424 China m3/ton 3,271 13,668 6,299 5,805 3,289 India m3/ton 7,437 16,869 5,350 8,426 7,824 Indonesia m3/ton 5,157 25,942 2,756 11,893 4,615 Malaysia m3/ton 4,564 32,534 2,859 10,654 3,749 New Zealand m3/ton 1,995 9,183 1,975 5,452 1,115 Pakistan m3/ton 6,300 24,803 - 14,124 6,672 Philippines m3/ton 5,235 22,033 5,228 14,306 5,460 South Africa m3/ton 3,553 12,220 9,399 6,688 3,968 Thailand m3/ton 4,392 26,049 6,760 12,395 4,383 USA m3/ton 1,680 14,041 6,612 10,235 1,447 Viet Nam m3/ton 5,099 20,312 5,178 - 3,859 Data source: Mekonnen and Hoekstra (2010)

F.2.3 Production system – Industrial

Country Unit Poultry Beef Pork Mutton Hen eggs

World Average m3/ton 2,231 10,244 5,225 5,623 2,872 Australia m3/ton 2,603 5,130 9,215 - 1,798 Brazil m3/ton 3,078 8,813 8,924 5,204 3,866 Canada m3/ton 1,781 9,745 4,978 - 1,556 China m3/ton 2,111 13,089 4,940 2,839 2,919 India m3/ton 3,668 14,749 12,272 5,600 4,484 Indonesia m3/ton 3,105 11,592 8,900 5,283 3,512 Malaysia m3/ton 2,851 11,496 7,085 6,886 2,851 New Zealand m3/ton 1,026 2,912 2,039 - 1,218 Pakistan m3/ton 3,044 8,783 0 6,757 3,796 Philippines m3/ton 3,120 11,453 8,351 6,753 4,153 South Africa m3/ton 2,201 9,177 7,475 3,478 2,854 Thailand m3/ton 2,570 9,440 8,026 5,907 3,333 USA m3/ton 1,723 3,856 4,601 - 1,581 Viet Nam m3/ton 2,981 5,576 5,781 - 2,938 Data source: Mekonnen and Hoekstra (2010)

Appendix G – Annual gross virtual water import flows

G.1 Food classification

Year Unit Cereals Vegetables Fruits Roots Livestock Total 2002 m3/yr 1,629,405,230 142,083,427 406,175,131 87,899,212 1,036,855,205 3,302,418,204 2003 m3/yr 1,306,842,184 137,514,639 410,621,612 96,673,166 1,207,820,924 3,159,472,524 2004 m3/yr 1,243,334,509 139,734,268 410,988,537 99,920,487 1,228,555,363 3,122,533,164 2005 m3/yr 1,085,397,364 145,514,024 395,914,761 129,005,333 1,507,346,683 3,263,178,166 2006 m3/yr 1,160,665,054 148,495,726 382,155,520 110,672,362 1,550,008,594 3,351,997,256 2007 m3/yr 1,284,817,675 150,515,353 382,005,847 103,728,048 1,789,294,438 3,710,361,361 2008 m3/yr 1,250,390,884 151,455,376 393,005,893 105,580,706 1,784,815,297 3,685,248,156 2009 m3/yr 1,237,365,446 160,128,428 396,010,635 114,355,635 1,756,114,216 3,663,974,361 2010 m3/yr 1,308,354,175 164,314,421 370,396,362 114,574,137 1,793,780,053 3,751,419,149 2011 m3/yr 1,428,844,656 165,278,842 384,209,948 121,810,743 1,617,259,335 3,717,403,524 Average m3/yr 1,293,541,718 150,503,450 393,148,425 108,421,983 1,527,185,011 3,472,800,587

G.2 Food items

No. Food Items m3/yr % Share Cereals 1 Wheat 342,556,070 9.86% 2 Rice 894,581,125 25.76% 3 Cereal unmilled 19,774,255 0.57% 4 Barley 266,129 0.01% 5 Maize 31,324,586 0.90% 6 Oats 448,664 0.01% 7 Oats rolled 4,590,890 0.13% Sub-total 1,293,541,718 37.25%

Vegetables 8 Cabbages and other brassicas 20,366,386 0.59% 9 Asparagus 2,516,127 0.07% 10 Lettuce and chicory 2,985,932 0.09% 11 Spinach 2,801,573 0.08% 12 Tomatoes 7,260,727 0.21% 13 Cauliflowers and broccoli 4,317,929 0.12% 14 Cucumbers and gherkins 5,640,853 0.16% 15 Eggplants (aubergines) 3,138,517 0.09% 16 Chillies and peppers, green 4,995,913 0.14% 17 Onions, dry 16,502,334 0.48% 18 Garlic 6,282,695 0.18% 19 Beans, green 6,698,789 0.19% 20 Peas, green 469,615 0.01% 21 Carrots and turnips 5,831,625 0.17% 22 Vegetables, fresh 49,118,368 1.41% 23 Watermelons 8,479,761 0.24% 24 Melons, other (inc.cantaloupes) 3,096,308 0.09% Sub-total 150,503,450 4.33%

Fruits 25 Bananas 35,001,130 1.01% 26 Oranges 22,526,379 0.65% 27 Tangerines, mandarins, clementines, satsumas 17,137,488 0.49% 28 Lemons and limes 10,422,460 0.30% 29 Grapefruit (inc. pomelos) 10,349,226 0.30% 30 Apples 33,862,634 0.98% 31 Pears 25,989,001 0.75% 32 Apricots 71,917 0.00% 33 Cherries 1,528,431 0.04% 34 Peaches and nectarines 1,988,907 0.06% 35 Plums and sloes 3,290,414 0.09% 36 Strawberries 522,904 0.02% 37 Grapes 5,424,922 0.16% 38 Figs dried 826,621 0.02%

39 Mangoes, mangosteens, guavas 52,815,975 1.52% 40 Avocados 1,242,941 0.04% 41 Pineapples 3,551,439 0.10% 42 Dates 2,939,478 0.08% 43 Kiwi fruit 1,285,096 0.04% 44 Papayas 31,521,759 0.91% 45 Fruit, tropical fresh 130,849,303 3.77% Sub-total 393,148,425 11.32%

Roots and tubers 46 Potatoes 12,252,938 0.35% 47 Sweet potatoes 4,398,033 0.13% 48 Starch, cassava 88,182,052 2.54% 49 Roots and tubers 3,588,959 0.10% Sub-total 108,421,983 3.12%

Livestock Beef 50 Meat, cattle 23,947,781 0.69% 51 Meat, cattle, boneless (beef & veal) 302,675,545 8.72% 52 Live cattle 148,224 0.00%

Mutton 53 Meat, goat 52,580,742 1.51% 54 Live goat 767,184 0.02% 55 Meat, sheep 60,361,429 1.74% 56 Live sheep 1,120,010 0.03%

Poultry meat 57 Meat, chicken 253,683,152 7.30% 58 Live chicken 227,182,460 6.54% 59 Meat, duck 1,685,160 0.05% 60 Live duck 32,907,153 0.95%

Pork 61 Meat, pork 293,512,837 8.45% 62 Live pig 109,092,654 3.14%

63 Hen eggs 167,520,680 4.82% Sub-total 1,527,185,011 43.98%

Grand total 3,472,800,587 100.00%

G.3 Food producing countries

No. Countries Unit Cereals Vegetables Fruits Roots Livestock Total % Share Cum. % 1. Thailand m3/yr 618,435,492 6,876,082 67,124,474 83,982,996 1,327,462 777,746,506 22.40% 22.40% 2. Malaysia m3/yr 14,171,975 82,987,466 168,467,755 2,318,752 422,661,683 690,607,631 19.89% 42.28% 3. Brazil m3/yr 586,040,959 586,040,959 16.88% 59.16% 4. Australia m3/yr 180,084,936 6,591,065 13,357,802 211,885,864 411,919,667 11.86% 71.02% 5. USA m3/yr 141,957,166 1,886,461 13,653,723 2,782,978 56,320,049 216,600,376 6.24% 77.26% 6. India m3/yr 156,895,790 8,278,288 2,398,120 240,012 182 167,812,392 4.83% 82.09% 7. China m3/yr 9,443,487 32,983,872 79,757,084 7,588,010 30,680,761 160,453,214 4.62% 86.71% 8. Viet Nam m3/yr 125,475,568 2,481,693 9,592,174 6,297,986 1,452,565 145,299,986 4.18% 90.89% 9. Indonesia m3/yr 681,160 6,253,450 767,602 2,940,368 109,099,326 119,741,905 3.45% 94.34% 10. New Zealand m3/yr 12,522 794,148 2,228,555 211,102 39,888,547 43,134,874 1.24% 95.58% 11. Canada m3/yr 32,436,721 77,636 10,544,734 43,059,092 1.24% 96.82% 12. Netherlands m3/yr 179,221 397,962 19,650 113,206 21,711,504 22,421,543 0.65% 97.47% 13. Philippines m3/yr 2,091,241 484,043 16,377,396 18,952,681 0.55% 98.01% 14. France m3/yr 544,486 767 13,728,884 14,274,137 0.41% 98.42% 15. South Africa m3/yr 145,565 10,930,073 799,357 11,874,995 0.34% 98.77% 16. Uruguay m3/yr 10,836,006 10,836,006 0.31% 99.08% 17. Myanmar m3/yr 8,564,084 8,564,084 0.25% 99.32% 18. Denmark m3/yr 4,892,379 4,892,379 0.14% 99.47% 19. Argentina m3/yr 592,175 684,674 2,810,508 4,087,357 0.12% 99.58% 20. Pakistan m3/yr 414,783 2,735,799 309,825 3,460,407 0.10% 99.68% 21. Belgium m3/yr 1,936,084 1,936,084 0.06% 99.74% 22. Egypt m3/yr 1,370,075 1,370,075 0.04% 99.78% 23. Japan m3/yr 1,082,565 102,298 26,856 1,211,719 0.03% 99.81%

24. Bangladesh m3/yr 275,254 849,009 1,124,263 0.03% 99.85% 25. Korea m3/yr 443,141 78,745 404,608 926,493 0.03% 99.87% 26. United Kingdom m3/yr 821,785 821,785 0.02% 99.90% 27. Chile m3/yr 726,416 726,416 0.02% 99.92% 28. Iran m3/yr 712,152 712,152 0.02% 99.94% 29. United Arab Emirates m3/yr 482,134 482,134 0.01% 99.95% 30. Germany m3/yr 342,395 77,529 419,923 0.01% 99.96% 31. Israel m3/yr 413,429 413,429 0.01% 99.97% 32. Italy m3/yr 385,825 6,468 392,293 0.01% 99.99% 33. Turkey m3/yr 158 265,675 18,416 284,249 0.01% 99.99% 34. Spain m3/yr 74,138 59,188 29,561 162,887 0.00% 100.00% 35. Mexico m3/yr 30,284 30,284 0.00% 100.00% 36. Hungary m3/yr 4,530 4,530 0.00% 100.00% 37. Finland m3/yr 1,494 1,494 0.00% 100.00% 38. Ireland m3/yr 188 188 0.00% 100.00% Total m3/yr 1,293,541,718 150,503,450 393,148,425 108,421,983 1,527,185,011 3,472,800,587 100.00%

Appendix H – Falkenmark Water Stress Index

Total water Total renewable withdrawal per water resources Ranking Country Condition capita per capita m3/cap/yr m3/cap/yr 1 Singapore 35 111 Absolute Scarcity 2 South Africa 271 973 Scarcity 3 Denmark 118 1,068 Stress 4 Pakistan 1,024 1,355 Stress 5 India 615 1,526 Stress 6 China 406 2,005 No stress 7 France 508 3,282 No stress 8 Philippines 843 4,868 No stress 9 Netherlands 635 5,430 No stress 10 Thailand 867 6,545 No stress 11 Indonesia 527 8,080 No stress 12 USA 1,575 9,589 No stress 13 Viet Nam 948 9,643 No stress 14 Malaysia 418 19,517 No stress 15 Australia 846 21,077 No stress 16 Argentina 920 21,141 No stress 17 Myanmar 675 21,931 No stress 18 Brazil 377 43,157 No stress 19 Uruguay 1,100 50,543 No stress 20 New Zealand 1,200 72,570 No stress 21 Canada -- 82,485 No stress Data source: World Bank

Appendix I – Water Scarcity Index

Ranking Country Criticality Ratio Technical water stress 1 Pakistan 0.74 High water stress 2 India 0.40 Medium high water stress 3 Singapore 0.32 Medium high water stress 4 South Africa 0.24 Medium high water stress 5 China 0.20 Medium low water stress 6 Philippines 0.17 Medium low water stress 7 USA 0.16 Medium low water stress 8 France 0.15 Medium low water stress 9 Thailand 0.13 Medium low water stress 10 Netherlands 0.12 Medium low water stress 11 Denmark 0.11 Medium low water stress 12 Viet Nam 0.09 Low water stress 13 Indonesia 0.06 Low water stress 14 Argentina 0.04 Low water stress 15 Australia 0.04 Low water stress 16 Myanmar 0.03 Low water stress 17 Uruguay 0.02 Low water stress 18 Malaysia 0.02 Low water stress 19 New Zealand 0.01 Low water stress 20 Brazil 0.01 Low water stress 21 Canada - - Data source: World Bank

Appendix J – Water Poverty Index

Appendix J.1 – Access Index

Access to Access to improved improved water sanitation facilities Arable land equipped for Country sources (%) Sub -Index (%) Sub-Index irrigation (%) Sub-Index Index (Average) Score Argentina 98.70 0.91 97.20 0.96 6.10 0.04 0.64 12.75 Australia 100.00 1.00 100.00 1.00 5.60 0.04 0.68 13.58 Brazil 97.50 0.83 81.30 0.71 7.50 0.06 0.53 10.66 Canada 99.80 0.99 99.80 1.00 2.00 0.00 0.66 13.22 China 91.90 0.46 65.30 0.46 63.50 0.63 0.52 10.33 Denmark 100.00 1.00 100.00 1.00 17.80 0.16 0.72 14.41 France 100.00 1.00 100.00 1.00 14.20 0.12 0.71 14.16 India 92.60 0.51 36.00 0.00 42.60 0.41 0.31 6.16 Indonesia 84.90 0.00 58.80 0.36 28.60 0.27 0.21 4.18 Malaysia 99.60 0.97 95.70 0.93 39.70 0.38 0.76 15.27 Myanmar 85.70 0.05 77.40 0.65 21.30 0.20 0.30 5.98 Netherlands 100.00 1.00 100.00 1.00 48.00 0.47 0.82 16.46 New Zealand 100.00 1.00 100.00 1.00 1.00 20.00 Pakistan 91.40 0.43 47.60 0.18 96.40 0.96 0.52 10.50 Philippines 91.80 0.46 74.30 0.60 29.00 0.28 0.44 8.87 Singapore 100.00 1.00 100.00 1.00 1.00 20.00 South Africa 95.10 0.68 74.40 0.60 13.20 0.11 0.46 9.27 Thailand 95.80 0.72 93.40 0.90 40.00 0.39 0.67 13.38 USA 99.20 0.95 100.00 1.00 17.10 0.15 0.70 14.01 Uruguay 99.50 0.97 96.40 0.94 13.60 0.12 0.68 13.53 Viet Nam 95.00 0.67 75.00 0.61 71.70 0.71 0.66 13.26 Data source: World Bank

Appendix J.2 – Capacity Index

Mortality rate, GDP per Education Gini under-5 (per capita, PPP Index Country index Sub-Index Index Sub-Index 1,000 live births) Sub-Index (US$) LOG(GDP) Sub- Index (Average) Score Argentina 0.78 0.74 0.44 0.56 13.30 0.87 0.73 14.51 Australia 0.93 1.00 0.34 0.81 4.00 0.99 42,809.90 4.63 0.80 0.90 17.97 Brazil 0.66 0.52 0.53 0.32 13.70 0.87 14,555.10 4.16 0.41 0.53 10.63 Canada 0.85 0.86 0.34 0.82 5.20 0.97 41,888.80 4.62 0.79 0.86 17.21 China 0.61 0.43 0.37 0.73 12.70 0.88 11,782.70 4.07 0.34 0.60 11.91 Denmark 0.87 0.90 0.27 1.00 3.50 0.99 41,524.50 4.62 0.78 0.92 18.39 France 0.82 0.80 0.32 0.87 4.20 0.98 35,969.10 4.56 0.73 0.85 16.95 India 0.47 0.18 0.34 0.82 52.70 0.40 5,238.00 3.72 0.05 0.36 7.27 Indonesia 0.60 0.42 0.38 0.70 29.30 0.68 9,254.40 3.97 0.25 0.51 10.27 Malaysia 0.67 0.54 0.46 0.49 8.50 0.93 22,555.80 4.35 0.57 0.63 12.66 Myanmar 0.37 0.00 50.50 0.42 0.21 4.23 Netherlands 0.89 0.94 0.29 0.95 4.00 0.99 41,979.90 4.62 0.79 0.92 18.31 New Zealand 0.92 0.98 6.30 0.96 32,768.20 4.52 0.70 0.88 17.60 Pakistan 0.37 0.00 0.30 0.93 85.50 0.00 4,549.30 3.66 0.00 0.23 4.65 Philippines 0.61 0.43 0.43 0.58 29.90 0.67 6,324.60 3.80 0.12 0.45 8.98 Singapore 0.77 0.71 2.80 1.00 76,236.80 4.88 1.00 0.90 18.09 South Africa 0.70 0.58 0.65 0.00 43.90 0.50 12,105.60 4.08 0.35 0.36 7.16 Thailand 0.61 0.43 0.39 0.67 13.10 0.88 13,931.80 4.14 0.40 0.59 11.86 USA 0.89 0.93 0.41 0.63 6.90 0.95 51,450.70 4.71 0.86 0.84 16.86 Uruguay 0.71 0.61 0.41 0.62 11.10 0.90 18,965.80 4.28 0.51 0.66 13.20 Vietnam 0.51 0.26 0.36 0.77 23.80 0.75 5,124.60 3.71 0.04 0.45 9.07 Data source: Education Index from UNDP (2013); rest of data from World Bank.

Appendix J.3 – Environmental Index

Country Fertilizer consumption Sub-index Agricultural methane emissions Sub-index Index Score (kg/ha of arable land) (% of total) (Average) Argentina 38.84 0.99 72.14 0.23 0.61 12.21 Australia 44.70 0.99 53.00 0.44 0.71 14.28 Brazil 181.74 0.95 73.80 0.21 0.58 11.60 Canada 74.36 0.98 25.86 0.73 0.86 17.17 China 647.62 0.81 35.92 0.62 0.72 14.36 Denmark 112.43 0.97 67.28 0.28 0.63 12.52 France 136.91 0.96 42.08 0.56 0.76 15.21 India 163.67 0.96 60.76 0.35 0.65 13.08 Indonesia 194.81 0.95 43.08 0.55 0.75 14.92 Malaysia 1,570.70 0.54 16.49 0.84 0.69 13.74 Myanmar 15.72 1.00 74.98 0.20 0.60 11.96 Netherlands 310.12 0.91 47.80 0.49 0.70 14.06 New Zealand 1,485.80 0.56 90.33 0.03 0.30 5.91 Pakistan 166.90 0.95 61.19 0.35 0.65 13.02 Philippines 113.52 0.97 61.92 0.34 0.66 13.10 Singapore 3,374.60 0.00 1.58 1.00 0.50 10.00 South Africa 62.00 0.99 30.75 0.68 0.83 16.67 Thailand 153.20 0.96 61.53 0.34 0.65 13.03 USA 131.11 0.97 37.28 0.61 0.79 15.75 Uruguay 192.60 0.95 92.91 0.00 0.47 9.47 Vietnam 297.05 0.92 52.11 0.45 0.68 13.63 Data source: World Bank

Appendix J.4 – Resources Index

Country Total renewable water IRWR1 per ERWR per capita ERWR2, reduced 1+2 LOG Index Score resources per capita capita (m3/capita/yr) by arbitrary factor (1+2) (m3/capita/yr) (m3/capita/yr) 50% Argentina 21,141 7,045 14,096 7,048 14,093 4.15 0.73 14.68 Australia 21,077 21,077 0 0 21,077 4.32 0.79 15.89 Brazil 43,157 28,254 14,903 7,452 35,706 4.55 0.87 17.49 Canada 82,485 81,007 1,478 739 81,746 4.91 1.00 20.00 China 2,005 1,986 19 10 1,996 3.30 0.44 8.75 Denmark 1,068 1,068 0 0 1,068 3.03 0.34 6.86 France 3,282 3,111 171 86 3,197 3.50 0.51 10.18 India 1,526 1,155 371 186 1,341 3.13 0.38 7.55 Indonesia 8,080 8,080 0 0 8,080 3.91 0.65 12.99 Malaysia 19,517 19,517 0 0 19,517 4.29 0.78 15.66 Myanmar 21,931 18,832 3,099 1,550 20,382 4.31 0.79 15.79 Netherlands 5,430 656 4,774 2,387 3,043 3.48 0.50 10.03 New Zealand 72,570 72,570 0 0 72,570 4.86 0.98 19.64 Pakistan 1,355 302 1,053 527 829 2.92 0.30 6.09 Philippines 4,868 4,868 0 0 4,868 3.69 0.57 11.46 Singapore 111 111 0 0 111 2.04 0.00 0.00 South Africa 973 849 124 62 911 2.96 0.32 6.38 Thailand 6,545 3,350 3,195 1,598 4,948 3.69 0.58 11.50 USA 9,589 8,805 784 392 9,197 3.96 0.67 13.38 Uruguay 50,543 27,062 23,481 11,741 38,803 4.59 0.89 17.74 Viet Nam 9,643 3,920 5,723 2,862 6,782 3.83 0.62 12.46 Data source: World Bank

Appendix J.5 – Use Index

Agriculture Industry Domestic Agriculture, freshwater Sub- Industry, freshwater Sub- freshwater value added withdrawals Ratio Index value added withdrawals Ratio Index withdrawals per Sub-Index Index Country (% of GDP) (% of total) (AGRI) (AGRI) (% of GDP) (% of total) (IND) (IND) capita (l/p/d) (DOM) (Average) Score Argentina 6.98 73.93 0.09 0.03 28.46 10.59 2.69 0.09 386.59 0.30 0.14 2.77 Australia 2.45 73.78 0.03 0.01 26.82 10.63 2.52 0.08 416.98 0.23 0.11 2.17 Brazil 5.71 60.00 0.10 0.03 24.98 17.00 1.47 0.04 235.34 0.61 0.23 4.58 Canada 0.00 0.00 China 10.01 64.61 0.15 0.05 43.89 23.21 1.89 0.06 136.33 0.85 0.32 6.40 Denmark 1.36 36.06 0.04 0.01 22.85 5.46 4.19 0.14 188.33 0.71 0.29 5.77 France 1.69 12.41 0.14 0.04 19.82 69.32 0.29 0.00 239.90 0.60 0.22 4.32 India 17.95 90.41 0.20 0.06 30.73 2.23 13.76 0.49 122.53 0.87 0.48 9.55 Indonesia 14.43 81.87 0.18 0.06 45.69 6.53 7.00 0.25 143.98 0.84 0.38 7.61 Malaysia 9.31 22.37 0.42 0.14 40.51 42.75 0.95 0.03 359.75 0.35 0.17 3.45 Myanmar 88.99 0.00 1.00 170.94 0.75 0.75 14.95 Netherlands 1.97 0.67 2.94 1.00 22.16 87.49 0.25 0.00 204.12 0.68 0.56 11.19 New Zealand 7.20 74.33 0.10 0.03 24.00 4.21 5.70 0.20 629.10 0.00 0.08 1.52 Pakistan 25.11 93.95 0.27 0.09 21.08 0.76 27.63 1.00 145.16 0.84 0.64 12.83 Philippines 11.23 82.23 0.14 0.04 31.12 10.12 3.08 0.10 173.62 0.74 0.30 5.92 Singapore 0.03 4.00 0.01 0.00 25.11 51.00 0.49 0.01 43.39 0.87 0.29 5.84 South Africa 2.32 62.69 0.04 0.01 29.90 6.05 4.94 0.17 201.20 0.68 0.29 5.77 Thailand 11.98 90.37 0.13 0.04 42.55 5.00 8.51 0.30 111.98 0.89 0.42 8.31 USA 1.30 40.22 0.03 0.01 21.00 46.11 0.46 0.01 567.18 0.00 0.01 0.10 Uruguay 9.96 86.61 0.11 0.04 25.40 2.19 11.62 0.42 329.63 0.42 0.29 5.78 Vietnam 18.38 94.78 0.19 0.06 38.31 3.75 10.22 0.36 36.83 0.74 0.39 7.76 Data source: World Bank

Appendix K – Virtual water flows related to trade in agricultural products

GVWI GVWE NVWI GVWI GVWE NVWI Country (Mm3/yr) (Mm3/yr) (Mm3/yr) Population (m3/cap/yr) (m3/cap/yr) (m3/cap/yr) Argentina 4,806 97,627 -92,821 37,031,000 130 2,636 -2,507 Australia 8,308 87,567 -79,260 19,387,700 428 4,517 -4,088 Brazil 33,419 110,316 -76,897 175,556,700 190 628 -438 Canada 29,957 81,816 -51,859 30,873,700 970 2,650 -1,680 China 107,603 91,637 15,965 1,313,008,400 82 70 12 Denmark 12,108 11,061 1,047 5,341,100 2,267 2,071 196 France 66,630 57,721 8,908 59,600,300 1,118 968 149 India 27,619 114,248 -86,629 1,050,639,600 26 109 -82 Indonesia 34,089 71,496 -37,407 210,595,400 162 339 -178 Malaysia 38,471 50,221 -11,751 23,617,700 1,629 2,126 -498 Myanmar 1,340 4,749 -3,409 48,414,400 28 98 -70 Netherlands 63,909 48,925 14,984 15,908,800 4,017 3,075 942 New Zealand 6,260 10,250 -3,990 3,903,000 1,604 2,626 -1,022 Pakistan 18,587 59,659 -41,072 144,732,500 128 412 -284 Philippines 17,619 17,450 168 78,493,800 224 222 2 Singapore 13,450 12,736 714 3,998,500 3,364 3,185 179 South Africa 12,387 11,459 928 45,206,900 274 253 21 Thailand 21,545 49,433 -27,888 62,663,900 344 789 -445 USA 183,814 288,901 -105,087 285,424,400 644 1,012 -368 Uruguay 1,098 7,560 -6,462 3,307,000 332 2,286 -1,954 Viet Nam 6,442 18,068 -11,626 81,215,200 79 222 -143 Data source: Gross virtual water flow data from Mekonnen and Hoekstra (2011); population average use FAO data. Period: 1996 – 2005